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Article

Recent French Export Performance

Author(s):
Alain Kabundi, and Francisco Nadal De Simone
Published Date:
January 2009
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I. Introduction

Evidence shows that a significant part of French activity fluctuations has a foreign source (e.g., Kose, Otrok, and Whiteman, 2003; and Kabundi and Nadal De Simone, 2007). In particular, since 2000, French foreign sector performance has experienced a substantial deterioration vis-à-vis its own past and relative to Germany. Some observers have suggested that the country has not benefited fully from the opportunities offered by the rapid economic performance of emerging Asian economies and the eastward expansion of the European Union (EU). Therefore, the question arises as to whether France is suffering from a competitiveness problem. This question has had, so far, an elusive answer. Traditional variables that explain international trade, such as the exchange rate, relative unit labor costs, and demand pressure seem insufficient to illuminate the recent decline in France’s export performance. Residuals from econometrically-estimated equations indicate a substantial drag on exports since 2001, not attributable to the standard global demand and price/cost factors.3

In addition, equilibrium exchange rate analysis indicates that France’s real effective exchange rate is largely in line with fundamentals. National account data show, however, that changes in export margins have cushioned the effects of the euro fluctuations. Cost competitiveness of French producers worsened in 2005 and early 2006, though it remains in line with its long-term average. Despite that producers lowered export prices in euros to maintain price competitiveness, the external position deteriorated during the period.

Hence, the relative underperformance of exports in past years may point to structural factors that leave French firms behind the global expansion. A more flexible economy should be able to reorient the destination of its exports and product mix toward fast-growing economies and sectors. Indeed, a sectoral study of total factor productivity (TFP) growth in manufacturing found that, while France does not lag significantly behind the United States in terms of level, TFP growth is hampered by the high ratio of minimum to median wages.4 Staff analysis also suggests that as France has become more sensitive to the global economy over time, it has tended to adjust more through changes in employment and productivity than through wage flexibility, strengthening the case for more structural reforms.5

This paper performs a descriptive analysis of French export data by destination and by SITC product classification distinguishing between the cyclical and the trend components of the series. Next, it analyzes the behavior of prices and quantities following a domestic and a foreign shock to the French economy; the paper contrasts and compares the reaction of French and German variables to shocks to unit labor costs and to terms of trade.

Globalization has greatly influenced economies over the past three decades. Countries’ boundaries have dropped through intensive trade of goods and services, and financial integration. Economies have benefited from trade and foreign direct investment (FDI). Conversely, globalization can make countries more vulnerable to external shocks. Crises can be severe and contagion can spread rapidly to other parts of the globe, as recently exemplified by the subprime crisis that started in the United States.

There is a consensus in the literature that globalization has largely positive effects. Globalization fosters comovement of macroeconomic variables across countries through trade integration and financial market integration (IMF, 2001; and Imbs, 2004). An increase in exports in one country boosts economic activity of the recipient country (Canova and Dellas, 1993; and IMF, 2001). Such spillover effects lead to high correlation of business cycles.

The integration of financial markets has also contributed to the synchronization of business cycles through the opening of countries’ capital accounts. Financial prices have become more synchronized through arbitrage (e.g., the global slowdown of 2000–01 was caused by the crash of the technology stock market in the United States). Financial comovements tend to be substantially larger than comovements in the real economy, and financial comovement has increased for financial markets over time (Brooks, Forbes, and Mody, 2003).

On the empirical front, most findings show increasing synchronization of economic variables across countries. Kose, Otrok, and Whiteman (2005), using the Bayesian dynamic factor model, extract common components in output, consumption, and investment and find that the degree of synchronization of business cycles of major macroeconomic aggregates across G-7 countries has increased over time. On the nature of shocks that drive the comovement, they find that oil-price shocks are behind synchronization of cycles during the “common shocks” period. Nadal-De Simone (2002), using a concordance index proposed by Harding and Pagan (2002) and the dynamic factor model of Stock and Watson (1991), finds evidence of a global component as well as a regional component that explains the comovement between European economies themselves and with the United States. In the same vein, Monfort and others (2004); Kose, Otrok, and Whiteman (2003); Malek Mansour (2003); Yang (2003); Lumsdaine and Prasad (2003); Bordo and Helbling (2004); and Canova, Ciccarelli, and Ortega (2007) support the view that fluctuations of most macroeconomic variables across developed countries are mainly driven by a global factor.

In contrast, Kose and Yi (2006); Kose, Prasad, and Terrones (2003); Stock and Watson (2003); and Heitz, Hild, and Monfort (2004) find that G-7 business cycles have become less synchronized. A possible reason is that trade flows could lead to increased specialization resulting in changes in the nature of business cycles. Trade ties are closely related to a rise in inter-industry specialization across nations, and then industry-specific shocks are the main driving forces of business cycles. Synchronization may be thus reduced. Similarly, international financial linkages could also stimulate production through the reallocation of capital in a manner consistent with countries’ comparative advantage (Imbs, 2004), which in turn reduces business cycle synchronization.

Other studies have emphasized the sources of shocks, their spillovers, and channels of their transmission. Recent examples include the study of the monetary transmission mechanism in the euro area using structural VAR analysis by Peersman (2005); Canova, Ciccarelli, and Ortega (2007); and Ciccarelli and Rebucci (2006). Similarly, Canova and Ciccarelli (2006), using a VAR with time-varying parameters, find a positive and significant effect of U.S. GDP growth shock on France and Italy, but a negligible effect on German GDP growth. Canova (2005) uses a structural VAR approach and finds that U.S. monetary shocks have a strong influence, while real supply and demand shocks have a minor effect. Given the limitations of the VAR methodology—the most conspicuous being that it cannot accommodate a large panel of series without the risk of running short of degrees of freedom—Stock and Watson (1998 and 2002) use the approximate structural dynamic factor model on a large panel of developed countries’ variables. Like Kabundi and Nadal De Simone (2007) and Eickmeier (2007), find a positive and significant effect of U.S. demand shocks on French and German output, while EU supply shocks tend to have important effects on French and German output.

The high degree of integration, and with it the exposure of countries to shocks, stresses the importance of good and factor markets flexibility. Economies’ flexibility to absorb domestic-and foreign origin shocks takes paramount importance, even more so when countries’ policy menu is restricted in some sense such as by participation in a currency area. Not surprisingly, competitiveness issues have been taken to the front line of the economic and political debate.

This study contains several findings. (1) Divergences in recent trade performance between France and Germany are not related to the cyclical part of trade but to its trend. (2) For most categories of products, France’s export cyclical component is less volatile than Germany’s. (3) In the 2000s, France’s trend export growth rate while higher than in the 1990s, was less than 60 percent Germany’s. (4) Both France and Germany faced a negative common factor in the 2000s, most likely due to the euro appreciation. (5) However, idiosyncratic factors were negative on average for France and positive for Germany. (6) The French economy seems less flexible to adjust to a negative shock to unit labor costs in manufacturing or to its terms of trade: the adjustment tends to be done relatively more via quantities than via prices suggesting the need to make labor and product markets as flexible as possible.

Section II discusses the data and elaborates on the methodology to deal with non-stationarity in the data. Section III describes the cyclical and trend components of French exports by destination and by product. Section IV analyzes the response of the French economy to a shock to unit labor costs in manufacturing and to the terms of trade. Section V discusses the policy implications of the study.

II. Data and Non-Stationarity

A. Data

This study uses two large data panels. The first one comprises 396 quarterly macroeconomic series and 106 Direction of Trade (DOT) series of trade by country (for a total of series N = 502). DOT series include imports and exports to the euro area, the EU, Ascension countries, Canada, the United States, the United Kingdom, Japan, China, Asia, Latin America, and the rest of the world. The second data panel contains 396 quarterly macroeconomic series and 110 series of trade by SITC Revision 3 category of products (for a total of series N = 506). The sample period is 1981:Q1–2006:Q4, or 104 observations for the two data panels (i.e., T = 104). The countries included in the sample are France, Germany, Japan, the Netherlands, the United Kingdom, and the United States. In addition to national variables, a set of global variables is included, containing such items as crude oil prices, a commodity industrial inputs price index, world demand, and world reserves. The variables cover the real sector of the economy including consumption, investment, international trade in goods and services, portfolio flows and FDI flows, prices, financial variables, and confidence indicators. All variables have had their seasonal component removed. The complete list of variables used in this study is in Annex I.

B. Dealing with Non-Stationarity

For estimation purposes, series have to be covariance stationary. Instead of applying unit root tests to determine the degree of integration of the series and then difference or detrend them depending on whether they are I(1) or I(0) with a deterministic trend, respectively, the Corbae-Ouliaris Ideal Band-Pass Filter was used. See Appendix I for a technical description of the filter. The reason for this approach is twofold. First, as is well known, currently available unit root tests have low power and often the decision on the degree of integration of the series has to be based on subjective judgment. Second, it is also known that first differencing removes a significant part of the variance of economic time series. Third, the ideal band-pass filter of Corbae and Ouliaris is consistent, is not subject to end-point problems and has no finite sampling error. As an illustration of these points, note the large share of variance that first differencing of French real GDP produces at the business cycle frequency band (between 6 and 32 quarters, according to the NBER definition of business cycles) (text figure).

France: Spectra of Real GDP Filtered

(Y axis: spectrum; X axis: periodicities in quarters)

III. Descriptive Part: Facts Without the Noise

Several interesting features of recent French export performance are clear from the data once the noise of short-term fluctuations is removed. First, the cyclical components of exports by country and products of France and Germany follow the same pattern, mimicking quite closely their business cycles (Figure 1). Export cycles of both countries portray a picture of negative growth in the early 1980s and 1990s, and at the end of the 1990s. The U.S. driven early-1980s recession, the European 1993 recession and the end of the stock market “bubble” at the end of the 1990s are clearly correlated to exports behavior (IMF, 2005). But, in general, France’s export cyclical component is less volatile than Germany’s, which may be associated with the product composition of both countries exports; German exports products have a higher short-term elasticity. Hence, divergences in recent trade performance between France and Germany do not seem to be related to the cyclical part of trade flows. What about the trend in trade flows?

Figure 1.Cyclical Part

Figure 2 shows annual trend growth of exports. Looking at exports by destination, it seems that Germany has benefited more from the excellent economic performance of China than France. Starting in 2002–03, French export performance is also weak relative to Germany in terms of exports to the EU, the euro area, the United States, and the United Kingdom. France’s export performance is also weaker relative to its own past. In the 1980s, French trend export growth dominated Germany’s only with respect to China; the reverse was true in the 1990s (Table 1). In the 2000s, France’s trend export growth rate, while higher than in the 1990s, was less than 60 percent of Germany’s.

Figure 2.Trend Part

Table 1.Trend Exports per Destination 1/(Average annual percent change)
1980-20061980-19891990-19992000-2006
FranceFrance to EU1.72.31.31.2
France to Asia2.12.41.71.9
France to Japan1.93.51.01.5
France to China3.85.62.43.8
France to Euro1.62.31.21.2
Franceto Accession Countries2.60.04.92.6
France to United States1.92.81.91.1
France to United Kingdom1.82.71.60.6
France to ROW0.90.30.61.7
GermanyGermany to EU1.92.50.92.2
Germany to Asia2.32.61.12.7
Germany to Japan2.23.90.31.5
Germany to China3.73.22.55.3
Germany to Euro1.82.50.62.1
Germany to Accession Countries3.22.14.43.0
Germany to United States2.32.81.92.2
Germany to United Kingdom2.03.01.21.8
Germany to ROW1.61.30.63.2

Numbers in bold indicate a higher growth rate of French trend exports.

Numbers in bold indicate a higher growth rate of French trend exports.

Table 2.Trend Exports per Product SITC 1/(Average annual percent change)
1980-20061980-19891990-19992000-2006
FranceTotal1.52.11.01.4
Food and live animal - SITC 00.91.30.70.5
Beverages and tobacco - SITC 11.52.40.81.4
Crude materials, inedible, except fuels - SITC 21.22.1-0.32.3
Mineral fuels, lubricants and related materials - SITC 31.5-0.11.53.8
Animal and vegetable oils, fats and waxes - SITC 41.20.80.62.3
Chemicals and related products - SITC 52.12.71.42.2
Manufactured goods - SITC 61.21.50.81.4
Machinery and transport equipment - SITC 71.72.61.21.1
Miscellaneous manufactured articles - SITC 81.72.51.01.7
Commodities and transactions - SITC 9-1.5-2.8-7.48.2
GermanyTotal1.92.31.02.5
Food and live animal - SITC 01.51.80.72.1
Beverages and tobacco - SITC 12.12.31.43.2
Crude materials, inedible, except fuels - SITC 21.82.10.52.8
Mineral fuels, lubricants and related materials - SITC 31.3-1.30.64.7
Animal and vegetable oils, fats and waxes - SITC 40.70.51.10.7
Chemicals and related products - SITC 52.02.31.12.8
Manufactured goods - SITC 61.62.00.62.4
Machinery and transport equipment - SITC 72.02.51.12.4
Miscellaneous manufactured articles - SITC 82.02.90.72.5
Commodities and transactions - SITC 93.01.33.52.9

Numbers in bold indicate a higher growth rate of French trend exports.

Numbers in bold indicate a higher growth rate of French trend exports.

The deterioration of French export performance vis-à-vis its own past and relative to Germany can be related to products exported. In the 1980s, French trend export growth dominated Germany’s in primary products, chemicals, and miscellaneous manufactured products; the situation was almost the opposite in the 1990s. In the 2000s, of the traditional French exports, France’s trend export growth rate was higher than Germany’s only in chemicals and “other” goods.

The analysis of trend growth rates suggests that there has been since 2002–03 a clear underperformance of French exports relative to the past and also relative to Germany. That France seems less competitive in recent years does not seem to be related to the euro; France’s underperformance is quite broad from a product viewpoint. The change in export performance is relatively recent, but has been protracted enough so as to raise the question of the competitiveness of the French economy. More analysis and time is needed, however, to conclude that there is a structural issue.

IV. Analytical Part: ULCM and TOT Shocks

A. The Model and Economic Conditions for Shocks Identification

To gain further insight into the possible causes of the deteriorating performance of the French foreign sector, this study uses a large dimensional approximate dynamic factor model following the static factor model of Stock and Watson (1998 and 2002).6 The methodology for estimating of the model comprises two main steps. First, estimating the common components of a large panel of data, and second, identifying a reduced number of structural shocks that explain the common components of the variables of interest.7 Once a decision is taken on the process followed by the common components, structural shocks have to be identified. The identification of structural shocks is achieved by focusing on the reduced form VAR residuals. Following Eickmeier (2007), the identification scheme has three steps. First, maximize the variance of the forecast error of the chosen variable and calculate impulse-response functions. Second, the identified shocks are assumed to be linearly correlated to a vector of fundamentals. Finally, orthogonal shocks are identified by rotation using a sign-identification strategy imposing inequality restrictions on the impulse-response functions of variables based on a typical aggregate demand/aggregate supply framework.8 Only those rotations among all possible rotations that have a structural meaning are chosen.

The choice of the variables of interest was motivated by two observations. First, France economic activity is largely influenced by developments in the rest of the world. Thus, it seemed natural to identify a terms of trade (TOT) shock to contrast and compare the behavior of France relative to Germany. Second, in the period of concern, only using unit labor cost measures of the REER, can be seen that French competitiveness deteriorated against Germany in the euro area, although it improved against some other countries. Wages have increased faster in France particularly at the bottom of the scale; these increases have been only partially compensated by higher productivity growth (text figure). Therefore, the second shock that was identified was a shock to unit labor costs in manufacturing (ULCM). The choice seemed also relevant in view of the results of the previous section.

Real effective exchange rates

(ULC based, 2002=100)

The text table displays the sign restrictions for the identification of shocks that are imposed contemporaneously and during the first year after the shock.

As in major standard macroeconomic models, an increase in ULCM can be interpreted as the result of a fall in labor productivity or an increase in labor compensation. The former is going to be interpreted as a supply shock and the later as a demand shock. This is consistent with the empirical observation that real wages are procyclical. Similarly, a rise in the TOT can result from a deterioration of the country’s competitiveness related to structural factors or alternatively from strong world demand for the country’s products. If the shock is persistent, it will result in an increase in consumption (and investment) and the current account will move into deficit. In contrast, if the TOT increase is due to strong world demand for the small country’s products, given the transient nature of the shock, consumers will largely save the windfall and the current account will move into surplus. Savings will increase.

Identification Inequalities
Increase in ULCM
Supply shockDemand shock
ULCM≥0≥0
Output≤0≥0
Real wages≤0≥0
Increase in Terms of Trade
Supply shockDemand shock
Terms of trade≥0≥0
Consumption≥0≤0
Current account≤0≥0

B. Estimation

The first step of the estimation is the determination of the number of factors. The estimation was done assuming that the series follow an approximate dynamic factor model.9 Using Bai and Ng’s (2002) selection criteria, four factors were retained. Not much can be concluded from the inspection of the factors and their loadings, however, because factors are identified only up to a rotation. Moreover, factors can be a linear combination not only of their contemporaneous values, but also of their lags.

Next, the identification of the structural shocks followed the approach of the structural VAR literature. No identification technology is completely foolproof, however. While the identification technology followed in this paper is flexible enough not to require special restrictions to disentangle common shocks from the contemporaneous transmission of regional or country-specific shocks, it does require additional work, for example, to confirm the nature and source of shocks. In order to properly distinguish a global (common) shock from the transmission within the same period of a country- or regional-specific shock, following Eickmeier (2007), this paper does not restrict the impact effect of the shock. Moreover, after identifying two shocks and giving them an economic interpretation, this study performs the same analysis on a data set containing only French variables. It finds that the impulse-responses of the French-only data set and the broader data set are similar, bringing thus further comfort as to the identification of the source of the shocks.

As it is well know in the literature, only two structural shocks could be identified for each variable of interest. The identification procedure proposed by Uhlig (2003) was applied to the common components of France and Germany’s ULCM and TOT so as to find a reduced number of structural shocks that maximizes the explanation of its forecast error variance over 20 periods.

Sign restrictions on impulse response functions were used to provide economic meaning to the structural shocks. Following Peersman (2005), the angle rotations were applied to the first two principal component shocks taking as pairs a supply shock and a demand shock. The bootstrap was made up of 500 draws.10 The impulse-response functions are calculated for the first five years to display the cyclical pattern associated with the structural shocks. Both the median response and a 90 percent bootstrapped confidence band are estimated.

Two final points on identification are necessary. First, the identification strategy followed in this study, by construction, extracts supply and demand shocks that maximize the explained forecast error variance of the common components of ULCM and TOT. Second, the impulse-response functions from a dataset containing only French variables were similar to those of the full sample, especially the supply shocks.

V. Econometric Results

Results are presented in the form of variance decomposition and impulse-response functions. Tables 3 and 4 show the variance decomposition and the forecast error variance of the common components (henceforth, error variance) of French and German variables explained by the two identified shocks to ULCM. Tables 5 and 6 show the same results for the two shocks to TOT. Figures in Annex II show the impulse-response functions of the French and German shocks to ULCM and TOT and their impact on French and German variables. These shocks suffice to explain up to 99 percent of the error variance of the common components of French and German ULCM over 20 quarters; similarly, these shocks explain up to 97 percent and 99 percent of the error variance of the common component of France and Germany TOT over 20 quarters, respectively. The variance shares of ULCM common components are high as they reach about 75 percent for both countries. In contrast, the variance shares of TOT are much smaller, especially for France: up to 10 percent and 42 percent for France and Germany, respectively. The later suggests that France’s TOT are more influenced than Germany’s by idiosyncratic factors. This is consistent with Kabundi and Nadal De Simone (2007) results: the TOT play a relatively lower role as channels of transmission of international disturbances in France than in Germany.

Table 3.Forecast Error Variance of the Common Components of French Variables Explained by the Supply and Demand Shocks to Unit Labor Costs in Manufacturing, 1980-2006 1/
Variance Shares

of the Common

Components
Supply

Shocks
Confidence IntervalsDemand

Shock
Confidence Intervals
Lower BoundUpper BoundLower BoundUpper Bound
1 GDP0.810.750.160.900.120.040.71
2 Personal consumption expenditure0.510.290.040.750.320.080.76
3 Private investment0.860.380.030.880.440.060.89
4 Employment0.700.740.200.890.080.030.58
5 Productivity0.350.830.100.920.130.040.79
6 Unit labor cost of the manufacturing sector0.740.070.020.620.930.360.98
7 Government savings0.870.430.030.890.430.050.90
8 Consumer confidence0.460.130.020.790.870.150.97
9 Industrial confidence0.470.150.040.530.540.200.84
10 Consumer prices0.900.050.000.600.830.300.94
11 Short-term interest rates0.580.900.220.920.050.020.57
12 Long-term interest rates0.590.410.060.800.070.010.47
13 M2 or M30.640.180.030.630.100.020.48
14 Stock prices0.770.490.010.820.220.010.81
15 Real compensation of employees0.620.770.240.890.070.030.54
16 SMIC0.610.110.010.610.810.300.95
17 TFP0.470.200.060.640.470.120.80
18 Exports total0.810.920.150.920.040.010.60
19 Imports total0.790.350.040.880.580.080.93
20 Terms of trade0.100.480.010.770.060.020.65
21 Real effective exchange0.790.610.010.910.310.010.83
22 Current account balance0.420.330.040.830.600.080.92
23 FDI out0.720.560.020.880.300.020.88
24 FDI in0.520.550.020.870.240.020.85
25 Exports to Euro0.830.260.010.720.570.060.89
26 Exports to EU0.840.280.010.720.580.070.90
27 Exports to EU accession ctrys0.690.110.000.680.680.200.91
28 Exports to United States0.440.440.010.740.170.000.74
29 Exports to United Kingdom0.740.380.010.810.570.070.93
30 Exports to Japan0.770.450.030.740.330.040.76
31 Exports to China,P.R.: Mainland0.160.430.020.810.440.040.86
32 Exports to Asia0.560.350.030.760.510.090.87
33 Exports to ROW0.640.370.020.810.600.120.95
34 EXP SITC Total0.900.640.010.690.090.090.88
35 EXP SITC 0: Food and live animal0.640.770.010.850.020.020.85
36 EXP SITC 1: Beverages and tobacco0.870.670.010.800.020.020.87
37 EXP SITC 2: Crude materials, inefible, except fuels0.910.710.030.670.110.140.85
38 EXP SITC 3: Mineral fuels, lubricants and related materials0.590.150.020.870.810.070.95
39 EXP SITC 4: Animal and vegetable oils, fats and waxes0.630.280.040.420.040.010.33
40 EXP SITC 5: Chemicals and related profucts, n.e.s0.920.750.020.750.050.050.83
41 EXP SITC 6: Manufactured goods0.920.710.020.710.090.120.88
42 EXP SITC 7: Machinery and transport equipment0.870.510.010.620.120.110.85
43 EXP SITC 8: Miscellaneous manufactured articles0.870.640.010.750.050.050.87
44 EXP SITC 9: Commodities and transactions n.e.c0.240.420.030.900.370.010.65

Forecast horizon is 20 quarters and refers to the levels of the series. Confidence intervals are constructed using bootstrapping methods.

Forecast horizon is 20 quarters and refers to the levels of the series. Confidence intervals are constructed using bootstrapping methods.

Table 4.Forecast Error Variance of the Common Components of German Variables Explained by the Supply and Demand Shocks to Unit Labor Costs in Manufacturing, 1980-2006 1/
Variance Shares

of the Common

Components
Supply

Shocks
Confidence IntervalsDemand

Shock
Confidence Intervals
Lower BoundUpper BoundLower BoundUpper Bound
1 GDP0.700.130.010.830.820.110.86
2 Personal consumption expenditure0.340.020.000.510.840.200.85
3 Private investment0.930.050.020.580.860.280.90
4 Employment0.770.050.010.690.790.110.83
5 Productivity0.360.300.030.890.450.010.62
6 Unit labor cost of the manufacturing sector0.730.340.000.620.660.350.96
7 Government savings0.700.010.010.620.880.200.88
8 Consumer confidence0.320.020.010.510.890.360.93
9 Industrial confidence0.540.250.040.480.620.300.87
10 Consumer prices0.920.690.020.860.030.010.77
11 Short-term interest rates0.760.070.020.770.880.150.89
12 Long-term interest rates0.540.380.040.760.430.020.64
13 M2 or M30.470.300.010.790.510.050.62
14 Stock prices0.690.010.010.550.870.220.86
15 Real compensation of employees0.540.610.030.890.310.040.50
16 Exports total0.690.150.010.790.810.140.87
17 Imports total0.840.040.010.600.890.280.91
18 Terms of trade0.420.700.060.910.050.020.75
19 Real effective exchange0.740.210.030.880.610.020.81
20 Current account balance0.170.050.010.620.010.020.38
21 FDI out0.520.320.010.600.400.110.85
22 FDI in0.150.010.010.600.860.200.88
23 Exports to Euro0.890.520.060.870.100.010.60
24 Exports to EU0.900.520.070.870.120.010.56
25 Exports to EU accession ctrys0.640.570.020.850.040.000.62
26 Exports to United States0.490.840.060.910.020.010.51
27 Exports to United Kingdom0.870.440.040.870.280.020.42
28 Exports to Japan0.810.630.040.920.190.030.48
29 Exports to China,P.R.: Mainland0.690.220.010.640.470.070.72
30 Exports to Asia0.760.560.030.900.290.030.44
31 Exports to ROW0.920.480.050.860.130.010.54
32 EXP SITC Total0.920.370.050.820.440.010.46
33 EXP SITC 0: Food and live animal0.920.380.030.810.360.000.42
34 EXP SITC 1: Beverages and tobacco0.580.370.010.790.210.000.32
35 EXP SITC 2: Crude materials, inefible, except fuels0.810.360.060.870.570.030.66
36 EXP SITC 3: Mineral fuels, lubricants and related materials0.640.080.020.550.860.410.92
37 EXP SITC 4: Animal and vegetable oils, fats and waxes0.410.280.010.660.210.040.44
38 EXP SITC 5: Chemicals and related profucts, n.e.s0.890.470.070.860.410.010.46
39 EXP SITC 6: Manufactured goods0.910.400.060.840.450.010.50
40 EXP SITC 7: Machinery and transport equipment0.890.370.030.810.390.000.41
41 EXP SITC 8: Miscellaneous manufactured articles0.920.360.030.810.360.000.41
42 EXP SITC 9: Commodities and transactions n.e.c0.080.090.010.680.680.030.74

Forecast horizon is 20 quarters and refers to the levels of the series. Confidence intervals are constructed using bootstrapping methods.

Forecast horizon is 20 quarters and refers to the levels of the series. Confidence intervals are constructed using bootstrapping methods.

Table 5.Forecast Error Variance of the Common Components of French Variables Explained by the Supply and Demand Shocks to Terms of Trade, 1980-2006 1/
Variance Shares

of the Common

Components
Supply

Shocks
Confidence IntervalsDemand

Shock
Confidence Intervals
Lower BoundUpper BoundLower BoundUpper Bound
1 GDP0.810.460.080.830.080.050.79
2 Personal consumption expenditure0.510.610.120.830.240.040.68
3 Private investment0.860.330.020.820.430.050.87
4 Employment0.700.390.080.810.050.060.75
5 Productivity0.350.370.020.800.140.070.87
6 Unit labor cost of the manufacturing sector0.740.070.020.740.220.000.53
7 Government savings0.870.290.010.810.460.060.88
8 Consumer confidence0.460.180.020.800.250.010.64
9 Industrial confidence0.470.380.110.810.040.000.38
10 Consumer prices0.900.010.000.600.260.000.58
11 Short-term interest rates0.580.190.020.720.030.050.79
12 Long-term interest rates0.590.250.020.760.120.020.47
13 M2 or M30.640.260.020.660.160.030.62
14 Stock prices0.770.020.000.550.630.240.90
15 Real compensation of employees0.620.250.030.700.050.090.80
16 SMIC0.610.020.010.610.200.000.48
17 TFP0.470.680.260.890.090.020.44
18 Exports total0.810.140.010.730.030.020.71
19 Imports total0.790.370.030.840.320.030.78
20 Terms of trade0.100.130.030.500.820.470.96
21 Real effective exchange0.790.000.000.420.100.000.46
22 Current account balance0.420.470.080.850.170.020.63
23 FDI out0.720.110.000.650.540.160.89
24 FDI in0.520.080.000.640.630.220.91
25 Exports to Euro0.830.050.010.690.160.000.33
26 Exports to EU0.840.040.010.690.160.000.33
27 Exports to EU accession ctrys0.690.000.000.540.240.000.54
28 Exports to United States0.440.010.000.460.520.230.86
29 Exports to United Kingdom0.740.040.010.700.120.000.37
30 Exports to Japan0.770.180.030.680.040.020.60
31 Exports to China,P.R.: Mainland0.160.080.020.680.080.010.47
32 Exports to Asia0.560.130.030.760.090.000.43
33 Exports to ROW0.640.050.020.690.110.000.35
34 EXP SITC Total0.900.390.010.690.030.010.32
35 EXP SITC 0: Food and live animal0.640.250.010.530.100.010.51
36 EXP SITC 1: Beverages and tobacco0.870.230.000.540.020.000.29
37 EXP SITC 2: Crude materials, inefible, except fuels0.910.570.050.770.070.010.54
38 EXP SITC 3: Mineral fuels, lubricants and related materials0.590.370.030.820.370.010.67
39 EXP SITC 4: Animal and vegetable oils, fats and waxes0.630.560.080.680.360.210.86
40 EXP SITC 5: Chemicals and related profucts, n.e.s0.920.410.020.650.090.010.52
41 EXP SITC 6: Manufactured goods0.920.420.020.690.030.010.38
42 EXP SITC 7: Machinery and transport equipment0.870.410.010.730.040.010.30
43 EXP SITC 8: Miscellaneous manufactured articles0.870.300.000.610.020.000.28
44 EXP SITC 9: Commodities and transactions n.e.c0.240.030.010.700.580.030.78

Forecast horizon is 20 quarters and refers to the levels of the series. Confidence intervals are constructed using bootstrapping methods.

Forecast horizon is 20 quarters and refers to the levels of the series. Confidence intervals are constructed using bootstrapping methods.

Table 6.Forecast Error Variance of the Common Components of German Variables Explained by the Supply and Demand Shocks Terms of Trade, 1980-2006 1/
Variance Shares

of the Common

Components
Supply

Shocks
Confidence IntervalsDemand

Shock
Confidence Intervals
Lower BoundUpper BoundLower BoundUpper Bound
1 GDP0.700.460.080.920.260.010.43
2 Personal consumption expenditure0.340.030.010.800.550.010.63
3 Private investment0.930.090.040.880.730.020.73
4 Employment0.770.270.060.880.530.010.63
5 Productivity0.360.300.010.790.360.050.82
6 Unit labor cost of the manufacturing sector0.730.060.030.740.760.070.81
7 Government savings0.700.090.020.860.550.010.60
8 Consumer confidence0.320.020.010.850.570.010.62
9 Industrial confidence0.540.230.050.690.210.010.52
10 Consumer prices0.920.040.000.300.930.640.98
11 Short-term interest rates0.760.320.070.910.440.010.53
12 Long-term interest rates0.540.160.070.780.140.020.61
13 M2 or M30.470.540.080.820.130.010.36
14 Stock prices0.690.070.020.840.580.010.63
15 Real compensation of employees0.540.700.070.840.120.040.71
16 Exports total0.690.540.120.910.330.010.48
17 Imports total0.840.110.050.890.710.020.69
18 Terms of trade0.420.110.010.430.880.560.99
19 Real effective exchange0.740.390.040.860.090.030.64
20 Current account balance0.170.150.010.430.530.060.70
21 FDI out0.520.020.010.650.880.220.91
22 FDI in0.150.070.010.880.450.000.51
23 Exports to Euro0.890.100.010.400.860.530.97
24 Exports to EU0.900.120.010.440.840.470.96
25 Exports to EU accession ctrys0.640.030.000.310.930.620.96
26 Exports to United States0.490.110.010.520.670.270.92
27 Exports to United Kingdom0.870.330.020.670.420.150.88
28 Exports to Japan0.810.420.020.690.440.200.88
29 Exports to China,P.R.: Mainland0.690.260.090.850.010.010.42
30 Exports to Asia0.760.540.040.800.270.090.82
31 Exports to ROW0.920.120.010.420.810.460.95
32 EXP SITC Total0.920.050.010.510.890.360.96
33 EXP SITC 0: Food and live animal0.920.030.000.480.890.380.96
34 EXP SITC 1: Beverages and tobacco0.580.020.000.460.790.240.93
35 EXP SITC 2: Crude materials, inefible, except fuels0.810.050.020.650.890.230.94
36 EXP SITC 3: Mineral fuels, lubricants and related materials0.640.250.050.800.690.050.78
37 EXP SITC 4: Animal and vegetable oils, fats and waxes0.410.200.040.770.080.000.31
38 EXP SITC 5: Chemicals and related profucts, n.e.s0.890.040.010.560.890.260.96
39 EXP SITC 6: Manufactured goods0.910.050.010.550.880.310.96
40 EXP SITC 7: Machinery and transport equipment0.890.050.010.490.870.390.96
41 EXP SITC 8: Miscellaneous manufactured articles0.920.040.000.470.890.380.96
42 EXP SITC 9: Commodities and transactions n.e.c0.080.190.010.620.790.330.95

Forecast horizon is 20 quarters and refers to the levels of the series. Confidence intervals are constructed using bootstrapping methods.

Forecast horizon is 20 quarters and refers to the levels of the series. Confidence intervals are constructed using bootstrapping methods.

The demand shocks to ULCM and TOT are relatively more important than supply shocks for both countries. Supply and demand shocks have qualitatively broadly similar responses in France and in Germany. However, the quantitative effects as well as the adjustment process are significantly different.

In both countries, supply shocks to ULCM reduce output, private consumption, investment and the volume of exports of goods and services. Employment falls, despite some downward adjustment of real wages. The real exchange rate appreciates. The consumer price index, however, clearly falls in Germany while it is flat in France. The impulse-response functions show that the negative effect on output and on the volume of exports and employment of supply shocks is larger in France than in Germany (there seems to be a relatively larger downward rigidity of wages in France). The SMIC has a tendency to rise somewhat despite the fall in labor productivity. While the dollar value of exports to all destinations increases in Germany, this is not the case in France (e.g., exports to the United States clearly fall). The total increase in the dollar value of French exports is half that of German exports. The same results are evident in terms of the euro value of exports per product, especially for manufactures, transport equipment and mineral fuels and lubricants. France’s euro value of exports is larger than Germany’s for beverages and tobacco, animal and vegetable oils, and commodities and transactions n.e.c. Therefore, France adjusts relatively less via price and wage changes, and more via employment changes than Germany.

Demand shocks to ULCM affect France and Germany differently. A demand shock to ULCM in France produces a short-term small increase in output while employment, real wages and the consumer price index rise without denting productivity. Exports volume tends to increase somewhat while the real exchange rate tends to depreciate. However, as productivity declines, the process is reversed. The value of exports to all destinations and for all products falls. In Germany, the same shock has a much shorter positive impact on output and employment, i.e., less than a year. The consumer price index increases much less than in France; the real wage increase is short lived and gets undone already after 1½ years. Exports volume decrease and the real exchange rate appreciates. The value of exports is not much affected. So, when ULCM increase due to demand pressures, the German economy adjusts more rapidly and seems to display less cost inertia. The real exchange rate helps to offset the negative effects on output and exports while in the case of France it magnifies them.

TOT shocks affect France less than Germany and that difference is more marked following a demand shock than following a supply shock. Positive supply shocks to TOT increase output, investment, and the volume of exports of goods and services. Employment rises, but in France it does so only after real wages have fallen somewhat, given that labor productivity does not change much. In Germany, employment rises sooner and more than in France; the German increase in labor productivity is relatively larger and offsets the rise in real wages enough so that ULCM fall. The real exchange rate depreciation is similar in both economies in the medium run, but it takes longer to reach that level in France than in Germany. The consumer price index falls somewhat in France and is flat in Germany. The dollar value of exports to all destinations has a tendency to fall in France, but the fall is more pronounced in Germany due to the larger short-run exchange rate depreciation experienced by the economy. Exports by product in euros show no major clear patterns, but there is in general a slight increase. Summarizing the results, supply shocks that increase the terms of trade are more consistent with a persistent supply shock in Germany than in France.

Positive demand shocks to TOT result in a negative output effect in France and are clearly inflationary. The real effective exchange rate appreciates as productivity falls and ULCM rise. The SMIC rises despite the fall in labor productivity. The dollar value of French exports by destination increases, except the value of exports to the United States and to accession countries. The increase is, however, larger for Germany, except in terms of exports to China. The euro value of French exports increases less than German exports. In fact, France’s exports are largely flat, except for crude materials, animal and vegetable oils, chemicals and commodities and transactions n.e.c. Overall, the results suggest that the French economy adapts less quickly to inflationary pressures on TOT as a result of a world demand.

A. The 1990s Until Today

The variance shares of ULCM and TOT remain basically the same for France, i.e., around 73 and 10 percent, respectively, during the shorter sample covering the period 1993–2006 (Table 7). The demand shocks to ULCM and TOT are still more significant than supply shocks. The relative importance of the channels of transmission changed. The variance shares of labor productivity and total factor productivity doubled; the variance shares of real compensation of employees, employment, and the SMIC also increased, while the share of consumer prices fell. The results suggest that most variables (except the price level) have become less influenced by idiosyncratic factors. In addition, the error variances indicate that in the recent sample, the role of demand shocks has increased. Similarly, the fall in the variance share of exports from 81 percent to 70 percent suggests that the foreign sector idiosyncratic factors play a more significant role in recent times, a result consistent with the analysis above.

Table 7.Forecast Error Variance of the Common Components of France Variables Explained by the Supply and Demand Shock to ULCM, 1993-2006 1/
Variance Shares

of the Common

Components
Supply

Shocks
Confidence IntervalsDemand

Shock
Confidence Intervals
Lower BoundUpper BoundLower BoundUpper Bound
1 GDP0.880.320.010.920.590.030.95
2 Personal consumption expenditure0.590.280.010.920.640.040.96
3 Private investment0.890.260.010.940.660.020.97
4 Employment0.840.270.030.870.620.030.90
5 Productivity0.720.780.010.740.100.040.89
6 Unit labor cost of the manufacturing sector0.730.130.000.950.790.020.98
7 Government savings0.890.310.010.930.590.020.96
8 Consumer confidence0.710.590.010.800.280.060.93
9 Industrial confidence0.600.290.010.670.430.080.89
10 Consumer prices0.860.190.010.930.680.020.97
11 Short-term interest rates0.670.310.040.860.490.040.86
12 Long-term interest rates0.710.380.020.630.450.010.63
13 M2 or M30.390.490.020.720.290.020.63
14 Stock prices0.940.610.010.820.210.020.86
15 Real compensation of employees0.790.320.050.860.470.040.84
16 SMIC0.710.080.000.950.810.020.98
17 TFP0.860.120.010.950.830.030.97
18 Exports total0.700.300.030.880.580.030.91
19 Imports total0.900.230.010.940.710.040.97
20 Terms of trade0.100.230.010.830.420.040.82
21 Real effective exchange0.800.370.020.650.010.050.70
22 Current account balance0.860.280.030.760.570.040.84
23 FDI out0.910.610.010.790.230.030.89
24 FDI in0.670.330.030.870.480.040.87
25 Exports to Euro0.860.100.010.680.420.080.87
26 Exports to EU0.880.100.010.680.410.080.87
27 Exports to EU accession ctrys0.780.020.000.880.710.040.94
28 Exports to United States0.860.770.020.640.060.050.82
29 Exports to United Kingdom0.870.210.010.690.200.080.86
30 Exports to Japan0.860.290.010.560.280.030.69
31 Exports to China,P.R.: Mainland0.570.290.010.680.080.060.82
32 Exports to Asia0.700.230.010.620.480.030.79
33 Exports to ROW0.700.270.010.720.160.080.87
34 EXP SITC Total0.930.040.010.680.930.080.89
35 EXP SITC 0: Food and live animal0.670.320.010.720.510.070.79
36 EXP SITC 1: Beverages and tobacco0.910.080.010.710.860.090.87
37 EXP SITC 2: Crude materials, inefible, except fuels0.930.070.010.660.900.050.87
38 EXP SITC 3: Mineral fuels, lubricants and related materials0.740.050.010.860.930.090.95
39 EXP SITC 4: Animal and vegetable oils, fats and waxes0.790.520.010.720.380.040.72
40 EXP SITC 5: Chemicals and related profucts, n.e.s0.940.150.000.700.820.060.83
41 EXP SITC 6: Manufactured goods0.940.070.010.680.900.070.88
42 EXP SITC 7: Machinery and transport equipment0.900.020.000.700.970.070.93
43 EXP SITC 8: Miscellaneous manufactured articles0.910.060.010.680.910.100.88
44 EXP SITC 9: Commodities and transactions n.e.c0.570.520.010.740.400.040.74

Forecast horizon is 20 quarters and refers to the levels of the series. Confidence intervals are constructed using bootstrapping methods.

Forecast horizon is 20 quarters and refers to the levels of the series. Confidence intervals are constructed using bootstrapping methods.

Table 8.Forecast Error Variance of the Common Components of Germany Variables Explained by the Supply and Demand Shock to ULCM, 1993-2006 1/
Variance Shares

of the Common

Components
Supply

Shocks
Confidence IntervalsDemand

Shock
Confidence Intervals
Lower BoundUpper BoundLower BoundUpper Bound
1 GDP0.900.140.020.590.430.290.91
2 Personal consumption expenditure0.770.250.010.560.300.120.81
3 Private investment0.950.050.010.450.790.490.96
4 Employment0.900.060.020.550.590.360.93
5 Productivity0.340.330.020.800.380.010.45
6 Unit labor cost of the manufacturing sector0.810.050.000.350.930.580.99
7 Government savings0.880.010.010.510.670.270.91
8 Consumer confidence0.590.040.000.330.870.500.94
9 Industrial confidence0.650.110.010.490.730.210.92
10 Consumer prices0.940.090.000.420.850.460.97
11 Short-term interest rates0.910.030.020.620.520.260.92
12 Long-term interest rates0.690.850.010.750.030.010.57
13 M2 or M30.720.260.010.630.280.060.67
14 Stock prices0.860.010.010.480.780.370.94
15 Real compensation of employees0.510.070.020.730.330.070.61
16 Exports total0.800.170.010.520.580.380.94
17 Imports total0.920.060.010.420.820.530.97
18 Terms of trade0.390.290.000.540.690.300.94
19 Real effective exchange0.640.010.020.710.260.100.79
20 Current account balance0.600.050.010.570.420.070.81
21 FDI out0.610.010.000.410.930.420.95
22 FDI in0.200.030.010.510.690.180.88
23 Exports to Euro0.910.120.010.610.640.200.90
24 Exports to EU0.920.120.010.630.600.180.88
25 Exports to EU accession ctrys0.760.120.000.560.720.280.92
26 Exports to United States0.740.010.030.760.240.080.52
27 Exports to United Kingdom0.910.090.020.720.340.090.68
28 Exports to Japan0.890.190.020.720.240.070.59
29 Exports to China,P.R.: Mainland0.670.060.010.470.760.390.96
30 Exports to Asia0.950.320.020.700.050.070.50
31 Exports to ROW0.940.160.000.630.590.190.88
32 EXP SITC Total0.950.100.010.670.450.130.87
33 EXP SITC 0: Food and live animal0.950.050.010.700.490.130.87
34 EXP SITC 1: Beverages and tobacco0.670.260.020.780.250.070.62
35 EXP SITC 2: Crude materials, inefible, except fuels0.850.110.010.640.490.160.89
36 EXP SITC 3: Mineral fuels, lubricants and related materials0.590.010.000.380.830.480.97
37 EXP SITC 4: Animal and vegetable oils, fats and waxes0.670.130.050.790.180.040.49
38 EXP SITC 5: Chemicals and related profucts, n.e.s0.940.140.010.700.410.120.83
39 EXP SITC 6: Manufactured goods0.920.100.010.680.450.130.86
40 EXP SITC 7: Machinery and transport equipment0.920.090.010.690.460.140.86
41 EXP SITC 8: Miscellaneous manufactured articles0.940.070.010.710.470.130.86
42 EXP SITC 9: Commodities and transactions n.e.c0.610.430.010.720.070.010.41

Forecast horizon is 20 quarters and refers to the levels of the series. Confidence intervals are constructed using bootstrapping methods.

Forecast horizon is 20 quarters and refers to the levels of the series. Confidence intervals are constructed using bootstrapping methods.

The relative more predominant role played by French idyosincratic factors becomes more obvious when France is compared with Germany. Germany had an increase in the variance share of ULCM and a slight decrease in the variance share of TOT. However, the price level, real compensation of employees, and productivity remained basically unchanged indicating that the role of idyosincratic factors did not change despite reunification. Idyosincratic factors have become more relevant for Germany in terms of the real effective exchange rate, as illustrated by a decline in its variance share. In contrast to France, German exports variance share increased significantly in the more recent period suggesting a more important role for common factors. Like for France, German demand shocks outweigh supply shocks.

VI. Conclusion and Policy Implications

French economic activity is significantly affected by economic activity in the rest of the world. One key channel for the transmission of shocks across countries is international trade. In recent years, the export performance of the French economy relative of its own past and relative to a major trading partner, Germany, has deteriorated. Therefore, the question arises as to whether France is suffering from a competitiveness problem. So far, traditional variables explaining international trade have proved to be insufficient to elucidate the recent decline in France’s export performance. Residuals from econometrically-estimated equations indicate a substantial drag on exports since 2001–02, not attributable to the standard global demand and price/cost factors. In addition, equilibrium exchange rate analysis indicates that France’s real effective exchange rate is largely in line with fundamentals.

This study has found that the recent deterioration of French export performance does not seem to be the related to the “cycle” but to the trend growth of exports, which seems lower in the early 2000s than it was in the past. France’s weaker export performance in the 2000s is reflected both in terms of geographical destination and in terms of product composition.

Given the exposure of the French economy to the rest of the world as well as the known asymmetry in the transmission of disturbances, it was natural to analyze the response of the French economy to typical domestic and foreign shocks. The analysis of the effects of an increase in unit labor costs in manufacturing and of an increase in the terms of trade, suggests that the French economy is relatively less flexible to adjust than the German economy. Faced with an upward shift in unit labor costs, France adjusts relatively less via price and wage changes, and more via employment changes. The same differences are also evident when both countries are faced with an upward TOT shock. To the extent that the convergence of the SMIC operated between 2003 and 2006 represented a significant increase in unit labor costs, and to the extent that the country is a price taker in most of its exports, the study supports the view that the difficulties observed in the French foreign sector may be structural.

The importance of trade flows and relative price changes in the international transmission of disturbances—as well as the policy constraints imposed by the euro area—highlight the relevance of domestic price flexibility. The French economy would benefit from further structural reforms that increase its good, service, and labor markets’ flexibility. This will matter for the magnitude of the real effective exchange rate changes, trade flows, and the size of the current account balance that will be necessary to accommodate the given disturbance.

Similarly, the analysis highlights the importance of measures that increase productivity and, in particular, the desirability of avoiding SMIC adjustments unrelated to productivity.

Appendix I. The Corbae-Ouliaris Ideal Band-Pass Filter

Let us assume that Xt is an I(1) process with ΔXt = vt such that vt has a Wold representation. The spectral density of vt is fvv (λ) >0, for all λ. The discrete Fourier transform of Xt for λt ≠ 0:

where λs=2πsn, s = 0, 1, …, n-1, are the fundamental frequencies. The second term makes it clear that the Fourier transform is not asymptotically independent across fundamental frequencies because the second term is a deterministic trend in the frequency domain with a random coefficient (XnX0)n1/2 Unless that term is removed, it will produce leakages into all frequencies λt ≠ 0, even in the limit as n →∞. Sacrificing a single observation, instead of estimating the random coefficient a la Hannan (1970), Corbae and Ouliaris (2006) show that by imposing that (XnX1) = (XnX0) will produce an estimate that will have no finite sampling error, has superior endpoint properties, and has much lower mean-squared error than popular time-domain filters such as HP or B-K. In addition, in contrast to B-K, it is consistent. This is the ideal band-pass filter used in the paper.

Appendix II. The Approximate Dynamic Factor Model

This study uses a large dimensional approximate dynamic factor model in the tradition of Stock and Watson (1998 and 2002). In contrast to the models of Sargent and Sims (1977) and Geweke (1977), it admits the possibility of serial correlation and weakly cross-sectional correlation of idiosyncratic components, as in Chamberlain (1983) and Chamberlain and Rothschild (1983). Similar models have recently been used by Giannone, Reichlin, and Sala (2002), Forni and others (2005), and Eickmeier (2006 and 2007).

A vector of time series Yt=(y1t, y2t,…, yNt)′ can be represented as the sum of two latent components, a common component Xt =(x1t,x2t,…,xNt)′ and an idiosyncratic component Ξt =1t, ε2t,…, εNt)‘

where Ft = (f1t, f2t,…, frt)′ is a vector of r common factors, and C=(c1,c2,,cN) is a N × r matrix of factor loadings, with r < <N. The common component Xt, which is a linear combination of common factors, is driven by few common shocks, which are the same for all variables. Nevertheless, the effects of common shocks differ from one variable to another due to different factor loadings. The idiosyncratic component is driven by idiosyncratic shocks, specific to each variable. The static factor model used here differs from the dynamic factor model in that it treats lagged or dynamic factors Ft as additional static factors. Thus, common factors include both lagged and contemporaneous factors.

Using the law of large number (as T, N → ∞), the idiosyncratic component, which is weakly correlated by construction, vanishes; and therefore, the common component can be easily estimated in a consistent manner by using standard principal component analysis. The first r eigenvalues and eigenvectors are calculated from the variance-covariance matrix cov(Yt).

and since the factor loadings C =V, equation (1) becomes,

From (1), the idiosyncratic component is

From all the more or less formal criteria to determine the number of static factors r, Bai and Ng (2002) information criteria was followed. As in Forni and others (2005), Ft was approximated by an autoregressive representation of order 111:

where B is a r × r matrix and ut a r × t vector of residuals. Equation (5) is the reduced form model of (1).

Once a decision is taken on the process followed by the common components, structural shocks have to be identified by focusing on the reduced form VAR residuals of (5). Following Eickmeier (2007), the identification scheme has three steps. First, maximize the variance of the forecast error of the chosen variable and calculate impulse-response functions. The interest here is unit labor costs in manufacturing (ULCM) and terms of trade. So, using ULCM as an example, a few major shocks driving unit them are identified.12 This implies maximizing the explanation of the chosen variance of the k-step ahead forecast error of ULCM with a reduced number of shocks.13 To this end, k -ahead prediction errors ut are decomposed into k mutually orthogonal innovations using the Cholesky decomposition. The lower triangular Cholesky matrix A is such that ut = Avt and E(vtvt)=I Hence,

The impulse-response function of yit to the identified shock in period k is obtained as follows:

with ci the ith row of factor loadings of C and with a corresponding variance-covariance matrix j=0kRijRij.

Second, the identified shocks are assumed to be linearly correlated to a vector of fundamentals. The fundamental forces ωt =1t, ω2t,…, ωrt)′ behind France’s ULCM are correlated to the identified shocks through the r × r matrix Q. Thus,

The intuition of the procedure is to select Q in such a way that the first shock explains as much as possible of the forecast error variance of the France’s ULCM common component over a certain horizon k, and the second shock explains as much as possible of the remaining forecast error variance. Focusing on the first shock, the task is to explain as much as possible of its error variance

where i is, in our example, the French ULCM, and q1 is the first column of Q. The column q1 is selected in such a way that q1σ2q1 is maximized, that is

where Sik=j=0k(k+1j)RijRij.

The maximization problem subject to the side constraint q1q1=1, can be written as the

Lagrangean,

where λ is the Lagrangean multiplier. From (10), q1 is the first eigenvector of Sik with eigenvalue λ and, therefore, the shock associated with q1 is the first principal component shock. Q is the matrix of eigenvectors of S, (q1, q2, …, qr), where ql (l=1,…,r) is the eigenvector corresponding to the lth principal component shock. Along the lines of Uhlig (2003), Eickmeier (2007), and Altig and others (2002), it is posed: k = 0 to k = 19, i.e., five years, which covers short- as well as medium-run dynamics.

Finally, orthogonal shocks are identified by rotation. If two shocks are identified, following Canova and de Nicoló (2003), the orthogonal shocks vector ωt = 1t2t)′ is multiplied by a 2 × 2 orthogonal rotation matrix P of the form:

where θ is the rotation angle; θ(0,π), produces all possible rotations and varies on a grid. If θ is fixed, and q = 5, there are q(q1)/2 bivariate rotations of different elements of the VAR. Following the insights of Sims and Zha (1999), and as in Peersman (2005), Canova and de Nicoló (2003), Eickmeier (2007), Kabundi and Nadal De Simone (2007) the number of angles between 0 and π is assumed to be 12: this implies 6,191,736,421x1010 (1210) rotations. Hence, the rotated factor wt = Pwt explains in total all the variation measured by the first two eigenvalues. This way the two principal components ωi are associated to the two structural shocks wi through the matrix P, and the impulse-response functions of the two structural shocks on all the fundamental forces can be estimated.

A sign-identification strategy is followed to identify the shocks. The method was developed by Peersman (2005). This strategy imposes inequality sign restrictions on the impulse response functions of variables based on a typical aggregate demand and aggregate supply framework.14 Only those rotations among all possible q × q rotations that have a structural meaning are chosen. The text table displays the sign restrictions for the identification of shocks that are imposed contemporaneously and during the first year after the shock.

Identification Inequalities
Increase in ULCM
Supply shockDemand shock
ULCM≥0≥0
Output≤0≥0
Real wages≤0≥0
Increase in Terms of Trade
Supply shockDemand shock
Terms of trade≥0≥0
Consumption≥0≤0
Current account≤0≥0
Annex I. Macroeconomic Series
Series No.Country/RegionVariable Name
1WorldCommodity Industrial Inputs Price Index, 1995 = 100, includes Agricultural Raw Materials and Metals Price Indices
2WorldCrude Oil (petroleum), simple average of three spot prices; Dated Brent, West Texas Intermediate, and the Dubai Fateh, US$ per barrel
3WorldExchange rate, U.S. dollars per national currency, period average
4WorldFed Funds
5WorldS&P 500
6WorldWorld demand
7WorldWorld reserves
8FrancePPI
9FranceReal Effective exchange rate, 2000 = 100, ULC-based
10FranceGross domestic product deflator
11FranceCPI
12FranceImport Unit Values/Import Prices
13FranceExport Unit Values/Export Prices
14FrancePrivate final consumption expenditure, volume
15FranceDependent employment
16FranceDependent employment of the business sector
17FranceGovernment employment
18FranceSelf-employed
19FranceTotal employment
20FranceEmployment of the business sector
21FranceExchange rate, index of US$ per local currency
22FranceGross domestic product, constant prices
23FrancePrivate non-residential fixed capital formation, volume
24FranceFixed investment in non-residential construction, volume
25FranceFixed investment in construction, volume
26FranceGovernment fixed capital formation, volume
27FrancePrivate residential fixed capital formation, volume
28FranceFixed investment in machinery & equipment, volume
29FranceIndustrial production
30FrancePrivate total fixed capital formation, volume
31FranceGross total fixed capital formation, volume
32FranceLabor force
33FranceImports of goods and services, volume, national accounts basis
34FranceLabor productivity of the total economy
35FranceHousehold saving, value
36FranceCurrent transfers received by households, value
37FranceUnit labor cost of the total economy
38FranceUnit labor cost of the manufacturing sector
39FranceUnemployment
40FranceWages, value
41FranceWages of the government sector, value
42FranceWage rate of the business sector
43FranceCompensation rate of government employees
44FranceWage rate of the manufacturing sector, hourly earnings
45FranceCompensation rate of the business sector
46FranceCompensation of employees, value
47FranceExports of goods and services, volume, national accounts basis
48FranceHousehold disposable income, real
49FranceProperty income received by households, value
50FranceGovernment current disbursements, value
51FranceCurrent disbursements of households, value
52FranceGovernment current receipts, value
53FranceCurrent receipts of households, value
54FranceSelf-employment income received by households, value
55FranceMoney supply, broad definition M2 or M3
56FranceCredit to private sector
57FranceHPI
58FranceSPI
59FranceTFP growth
60FranceDirect investment abroad
61FranceDir. invest. in rep. econ., n.i.e.
62FrancePortfolio investment assets
63FrancePortfolio investment liab., n.i.e
64FranceOther investment assets
65FranceOther investment liab., n.i.e
66FranceFinancial account, n.i.e.
67FranceCapacity utilization
68FranceBalance of income, value, balance of payments basis
69FranceCurrent account, value
70FranceCurrent account, value in US$
71FranceGovernment consumption of fixed capital, value
72FranceLong-term interest rate on government bonds
73FranceShort-term interest rate\PERCENT
74FranceIncrease in stocks, volume
75FranceLabor force participation rate
76FranceGovernment saving(net), value
77FranceHousehold saving ratio
78FranceUnemployment rate
79FranceIndustrial confidence
80FranceConsumer confidence
81FranceSmic
82FranceTerms of trade
83FranceReal compensation of employees
84GermanyPPI
85GermanyReal Effective exchange rate, 2000 = 100, ULC-based
86GermanyGross domestic product, deflator, market prices
87GermanyQ2-2007
88GermanyImport Unit Values/Import Prices
89GermanyExport Unit Values/Export Prices
90GermanyGovernment consumption of fixed capital, value
91GermanyPrivate final consumption expenditure, volume
92GermanyDependent employment
93GermanySelf-employed
94GermanyTotal employment
95GermanyExchange rate, index of US$ per local currency
96GermanyGross domestic product, volume, market prices
97GermanyPrivate non-residential fixed capital formation, volume
98GermanyFixed investment in non-residential construction, volume
99GermanyFixed investment in construction, volume
100GermanyGovernment fixed capital formation, volume
101GermanyPrivate residential fixed capital formation, volume
102GermanyFixed investment in machinery & equipment, volume
103GermanyIndustrial production
104GermanyPrivate total fixed capital formation, volume
105GermanyGross total fixed capital formation, volume
106GermanyLabor force
107GermanyImports of goods and services, volume, national accounts basis
108GermanyLabor productivity of the total economy
109GermanyHousehold saving, value
110GermanyCurrent transfers received by households, value
111GermanyUnit labor cost of the total economy
112GermanyUnit labor cost of the manufacturing sector
113GermanyUnemployment
114GermanyWages, value
115GermanyWages of the government sector, value
116GermanyCompensation of employees, value
117GermanyExports of goods and services, volume, national accounts basis
118GermanyFactor income from abroad, volume, balance of payments basis
119GermanyHousehold disposable income, real
120GermanyGovernment current disbursements, value
121GermanyCurrent disbursements of households, value
122GermanyGovernment current receipts, value
123GermanyCurrent receipts of households, value
124GermanyMoney supply, broad definition M2 or M3
125GermanyClaims on oth resid sector
126GermanyHPI
127GermanySPI
128GermanyDirect investment abroad
129GermanyDir. invest. in rep. econ., n.i.e.
130GermanyPortfolio investment assets
131GermanyPortfolio investment liab., n.i.e
132GermanyOther investment assets
133GermanyOther investment liab., n.i.e
134GermanyFinancial account, n.i.e.
135GermanyCapacity utilization
136GermanyBalance of income, value, balance of payments basis
137GermanyCurrent account, value
138GermanyCurrent account, value in US$
139GermanyLong-term interest rate on government bonds
140GermanyShort-term interest rate
141GermanyLabor force participation rate
142GermanyGovernment saving(net), value
143GermanyHousehold saving ratio
144GermanyUnemployment rate
145GermanyIndustrial confidence
146GermanyConsumer confidence
147GermanyTerms of trade
148GermanyStock prices
149GermanyReal compensation of employees
150JapanPPI
151JapanReal Effective exchange rate, 2000 = 100, ULC-based
152JapanGross domestic product deflator
153JapanCPI
154JapanImport Unit Values/Import Prices
155JapanExport Unit Values/Export Prices
156JapanPrivate final consumption expenditure, volume
157JapanDependent employment
158JapanDependent employment of the business sector
159JapanGovernment employment
160JapanSelf-employed
161JapanTotal employment
162JapanEmployment of the business sector
163JapanExchange rate, index of US$ per local currency
164JapanGross domestic product, volume, market prices
165JapanPrivate non-residential fixed capital formation, volume
166JapanGovernment fixed capital formation, volume
167JapanPrivate residential fixed capital formation, volume
168JapanIndustrial production
169JapanPrivate total fixed capital formation, volume
170JapanGross total fixed capital formation, volume
171JapanLabor force
172JapanImports of goods and services, volume, national accounts basis
173JapanMoney supply, broad definition: M2 or M3
174JapanFactor income paid abroad, volume, balance of payments basis
175JapanLabor productivity of the total economy
176JapanUnit labor cost of the manufacturing sector
177JapanUnemployment
178JapanWage rate of the manufacturing sector, hourly earnings
179JapanExports of goods and services, volume, national accounts basis
180JapanFactor income from abroad, volume, balance of payments basis
181JapanJPN Monetary aggregate M1 sa/Quantum (non-additive or stock
figures) SA\yen
182JapanClaims on private sector
183JapanHPI
184JapanSPI
185JapanDirect investment abroad
186JapanDir. invest. in rep. econ., n.i.e.
187JapanPortfolio investment assets
188JapanPortfolio investment liab., n.i.e
189JapanOther investment liab., n.i.e
190JapanFinancial account, n.i.e.
191JapanBalance of income, value, balance of payments basis
192JapanCurrent account, value
193JapanCurrent account, value in US$
194JapanLong-term interest rate on government bonds
195JapanShort-term interest rate
196JapanIncrease in stocks, volume
197JapanLabor force participation rate
198JapanUnemployment rate
199JapanVelocity of money
200JapanTerms of trade
201JapanIndustrial confidence
202JapanStock prices
203NetherlandsReal Effective exchange rate, 2000 = 100, ULC-based
204NetherlandsGross domestic product, deflator, market prices
205NetherlandsCPI
206NetherlandsImport Unit Values/Import Prices
207NetherlandsExport Unit Values/Export Prices
208NetherlandsGovernment consumption of fixed capital, value
209NetherlandsPrivate final consumption expenditure, volume
210NetherlandsTotal employment
211NetherlandsExchange rate, index of US$ per local currency
212NetherlandsGross domestic product, volume, market prices
213NetherlandsPrivate non-residential fixed capital formation, volume
214NetherlandsFixed investment in non-residential construction, volume
215NetherlandsFixed investment in construction, volume
216NetherlandsGovernment fixed capital formation, volume
217NetherlandsPrivate residential fixed capital formation, volume
218NetherlandsFixed investment in machinery & equipment, volume
219NetherlandsIndustrial production
220NetherlandsPrivate total fixed capital formation, volume
221NetherlandsGross total fixed capital formation, volume
222NetherlandsLabor force
223NetherlandsImports of goods and services, volume, national accounts basis
224NetherlandsLabor productivity of the total economy
225NetherlandsUnit labor cost of the total economy
226NetherlandsUnit labor cost of the manufacturing sector
227NetherlandsWage rate of the manufacturing sector, hourly earnings
228NetherlandsExports of goods and services, volume, national accounts basis
229NetherlandsGovernment current disbursements, value
230NetherlandsGovernment current receipts, value
231NetherlandsMoney supply, broad definition: M2 or M3
232NetherlandsClaims on private sector
233NetherlandsHPI
234NetherlandsSPI
235NetherlandsTFP growth
236NetherlandsDirect investment abroad
237NetherlandsDir. invest. in rep. econ., n.i.e.
238NetherlandsPortfolio investment assets
239NetherlandsPortfolio investment liab., n.i.e
240NetherlandsOther investment assets
241NetherlandsOther investment liab., n.i.e
242NetherlandsFinancial account, n.i.e.
243NetherlandsCapacity utilization
244NetherlandsBalance of income, value, balance of payments basis
245NetherlandsCurrent account, value
246NetherlandsCurrent account, value in US$
247NetherlandsLong-term interest rate on government bonds
248NetherlandsShort-term interest rate\PERCENT
249NetherlandsIncrease in stocks, volume
250NetherlandsLabor force participation rate
251NetherlandsGovernment saving(net), value
252NetherlandsUnemployment rate
253NetherlandsConsumer confidence
254NetherlandsTerms of trade
255NetherlandsStock prices
256UKPPI
257UKReal Effective exchange rate, 2000 = 100, ULC-based
258UKGross domestic product deflator
259UKCPI
260UKImport Unit Values/Import Prices
261UKExport Unit Values/Export Prices
262UKGovernment consumption of fixed capital, value
263UKPrivate final consumption expenditure, volume
264UKDependent employment
265UKDependent employment of the business sector
266UKGovernment employment
267UKSelf-employed
268UKTotal employment
269UKEmployment of the business sector
270UKExchange rate, index of US$ per local currency
271UKGross domestic product, constant prices
272UKPrivate non-residential fixed capital formation, volume
273UKGovernment fixed capital formation, volume
274UKPrivate residential fixed capital formation, volume
275UKIndustrial production
276UKPrivate total fixed capital formation, volume
277UKGross total fixed capital formation, volume
278UKLabor force
279UKImports of goods and services, volume, national accounts basis
280UKLabor productivity of the total economy
281UKHousehold saving, value
282UKCurrent transfers received by households, value
283UKUnit labor cost of the total economy
284UKUnit labor cost of the manufacturing sector
285UKUnemployment
286UKWages, value
287UKWage rate of the business sector
288UKCompensation rate of government employees
289UKWage rate of the manufacturing sector, hourly earnings
290UKCompensation rate of the business sector
291UKCompensation of employees, value
292UKExports of goods and services, volume, national accounts basis
293UKHousehold disposable income, real
294UKProperty income received by households, value
295UKGovernment current disbursements, value
296UKCurrent disbursements of households, value
297UKGovernment current receipts, value
298UKCurrent receipts of households, value
299UKSelf-employment income received by households, value
300UKMoney supply, broad definition: M2 or M3
301UKClaims on private sector
302UKHPI
303UKSPI
304UKFixed investment of government enterprises, volume
305UKDirect investment abroad
306UKDir. invest. in rep. econ., n.i.e.
307UKPortfolio investment assets
308UKPortfolio investment liab., n.i.e
309UKOther investment assets
310UKOther investment liab., n.i.e
311UKFinancial account, n.i.e.
312UKCapacity utilization
313UKBalance of income, value, balance of payments basis
314UKCurrent account, value
315UKCurrent account, value in US$
316UKLong-term interest rate on government bonds
317UKShort-term interest rate
318UKIncrease in stocks, volume
319UKLabor force participation rate
320UKGovernment saving(net), value
321UKHousehold saving ratio
322UKUnemployment rate
323UKIndustrial confidence
324UKTerms of trade
325UKConsumer confidence
326USAReal Effective exchange rate, 2000 = 100, ULC-based
327USAGross domestic product deflator
328USACPI
329USAImport Unit Values/Import Prices
330USAExport Unit Values/Export Prices
331USAPrivate final consumption expenditure, volume
332USADependent employment
333USADependent employment of the business sector
334USAGovernment employment
335USASelf-employed
336USATotal employment
337USAEmployment of the business sector
338USAGross domestic product, constant prices
339USAPrivate non-residential fixed capital formation, volume
340USAFixed investment in non-residential construction, volume
341USAFixed investment in construction, volume
342USAGovernment fixed capital formation, volume
343USAPrivate residential fixed capital formation, volume
344USAFixed investment in machinery & equipment, volume
345USAIndustrial production
346USAPrivate total fixed capital formation, volume
347USAGross total fixed capital formation, volume
348USALabor force
349USAImports of goods and services, volume, national accounts basis
350USALabor productivity of the total economy
351USACurrent transfers received by households, value
352USAUnit labor cost of the total economy
353USAUnit labor cost of the manufacturing sector
354USAUnemployment
355USAWages, value
356USAWages of the government sector, value
357USAWage rate of the business sector
358USACompensation rate of government employees
359USAWage rate of the manufacturing sector, hourly earnings
360USACompensation rate of the business sector
361USACompensation of employees, value
362USAExports of goods and services, volume, national accounts basis
363USAHousehold disposable income, real
364USAProperty income received by households, value
365USAGovernment current disbursements, value
366USACurrent disbursements of households, value
367USAGovernment current receipts, value
368USACurrent receipts of households, value
369USASelf-employment income received by households, value
370USAUSA Monetary aggregate M1 sa/Quantum (non-additive or stock figures) SA\dollars
371USAUSA Monetary aggregate M2 sa/Quantum (non-additive or stock figures) SA\dollars
372USAClaims on private sector
373USAHPI
374USASPI
375USADirect investment abroad
376USADir. invest. in rep. econ., n.i.e.
377USAPortfolio investment assets
378USAPortfolio investment liab., n.i.e
379USAOther investment assets
380USAOther investment liab., n.i.e
381USAFinancial account, n.i.e.
382USABalance of income, value, balance of payments basis
383USACurrent account, value in US$
384USALong-term interest rate on government bonds
385USALong-term interest rate on corporate bonds
386USAShort-term interest rate
387USAIncrease in stocks, volume
388USALabor force participation rate
389USAGovernment saving(net), value
390USAHousehold saving, value
391USAHousehold saving ratio
392USAUnemployment rate
393USAVelocity of money
394USAManufacturing - Industrial confidence indicator
395USAConsumer confidence indicator
396USATerms of trade
Trade by Destination
397FranceImports from Euro
398Trade by destinationImports from EU
399Imports from EU ascension countries
400Imports from Canada
401Imports from United States
402Imports from United Kingdom
403Imports from Japan
404Imports from China, P.R.: Mainland
405Imports from Asia
406Imports from Latam
407Imports from ROW
408Exports to Euro
409Exports to EU
410Exports to EU ascension ctrys
411Exports to Canada
412Exports to United States
413Exports to United Kingdom
414Exports to Japan
415Exports to China, P.R.: Mainland
416Exports to Asia
417Exports to Latam
418Exports to ROW
419GermanyImports from Euro
420Trade by destinationImports from EU
421Imports from EU ascension ctrys
422Imports from Canada
423Imports from United States
424Imports from United Kingdom
425Imports from Japan
426Imports from China, P.R.: Mainland
427Imports from Asia
428Imports from Latam
429Imports from ROW
430Exports to Euro
431Exports to EU
432Exports to EU ascension ctrys
433Exports to Canada
434Exports to United States
435Exports to United Kingdom
436Exports to Japan
437Exports to China, P.R.: Mainland
438Exports to Asia
439Exports to Latam
440Exports to ROW
441JapanImports from Euro
442Trade by destinationImports from EU
443Imports from EU ascension ctrys
444Imports from Canada
445Imports from United States
446Imports from United Kingdom
447Imports from China, P.R.: Mainland
448Imports from Asia
449Imports from Latam
450Imports from ROW
451Exports to Euro
452Exports to EU
453Exports to EU ascension ctrys
454Exports to Canada
455Exports to United States
456Exports to United Kingdom
457Exports to China, P.R.: Mainland
458Exports to Asia
459Exports to Latam
460Exports to ROW
461NetherlandsImports from Euro
462Trade by destinationImports from EU
463Imports from EU ascension ctrys
464Imports from Canada
465Imports from United States
466Imports from United Kingdom
467Imports from Japan
468Imports from China, P.R.: Mainland
469Imports from Asia
470Imports from Latam
471Imports from ROW
472Exports to Euro
473Exports to EU
474Exports to EU ascension ctrys
475Exports to Canada
476Exports to United States
477Exports to United Kingdom
478Exports to Japan
479Exports to China, P.R.: Mainland
480Exports to Asia
481Exports to Latam
482Exports to ROW
483USAImports from Euro
484Trade by destinationImports from EU
485Imports from EU ascension ctrys
486Imports from Canada
487Imports from United Kingdom
488Imports from Japan
489Imports from China, P.R.: Mainland
490Imports from Asia
491Imports from Latam
492Imports from ROW
493Exports to Euro
494Exports to EU
495Exports to EU ascension ctrys
496Exports to Canada
497Exports to United Kingdom
498Exports to Japan
499Exports to China, P.R.: Mainland
500Exports to Asia
501Exports to Latam
502Exports to ROW
Trade by Product
397FranceIMP SITC Total
398Trade by productIMP SITC 0: Food and live animal
399IMP SITC 1: Beverages and tobacco
400IMP SITC 2: Crude materials, inedible, except fuels
401IMP SITC 3: Mineral fuels, lubricants and related materials
402IMP SITC 4: Animal and vegetable oils, fats and waxes
403IMP SITC 5: Chemicals and related products, n.e.s.
404IMP SITC 6: Manufactured goods
405IMP SITC 7: Machinery and transport equipment
406IMP SITC 8: Miscellaneous manufactured articles
407IMP SITC 9: Commodities and transactions n.e.c.
408EXP SITC Total
409EXP SITC 0: Food and live animal
410EXP SITC 1: Beverages and tobacco
411EXP SITC 2: Crude materials, inedible, except fuels
412EXP SITC 3: Mineral fuels, lubricants and related materials
413EXP SITC 4: Animal and vegetable oils, fats and waxes
414EXP SITC 5: Chemicals and related products, n.e.s.
415EXP SITC 6: Manufactured goods
416EXP SITC 7: Machinery and transport equipment
417EXP SITC 8: Miscellaneous manufactured articles
418EXP SITC 9: Commodities and transactions n.e.c.
419GermanyIMP SITC Total
420Trade by productIMP SITC 0: Food and live animal
421IMP SITC 1: Beverages and tobacco
422IMP SITC 2: Crude materials, inedible, except fuels
423IMP SITC 3: Mineral fuels, lubricants and related materials
424IMP SITC 4: Animal and vegetable oils, fats and waxes
425IMP SITC 5: Chemicals and related products, n.e.s.
426IMP SITC 6: Manufactured goods - Germany
427IMP SITC 7: Machinery and transport equipment
428IMP SITC 8: Miscellaneous manufactured articles
429IMP SITC 9: Commodities and transactions n.e.c.
430EXP SITC Total
431EXP SITC 0: Food and live animal
432EXP SITC 1: Beverages and tobacco
433EXP SITC 2: Crude materials, inedible, except fuels
434EXP SITC 3: Mineral fuels, lubricants and related materials
435EXP SITC 4: Animal and vegetable oils, fats and waxes
436EXP SITC 5: Chemicals and related products, n.e.s.
437EXP SITC 6: Manufactured goods
438EXP SITC 7: Machinery and transport equipment
439EXP SITC 8: Miscellaneous manufactured articles
440EXP SITC 9: Commodities and transactions n.e.c.
441JapanIMP SITC Total
442Trade by productIMP SITC 0: Food and live animal
443IMP SITC 1: Beverages and tobacco
444IMP SITC 2: Crude materials, inedible, except fuels
445IMP SITC 3: Mineral fuels, lubricants and related materials
446IMP SITC 4: Animal and vegetable oils, fats and waxes
447IMP SITC 5: Chemicals and related products, n.e.s.
448IMP SITC 6: Manufactured goods - Japan
449IMP SITC 7: Machinery and transport equipment
450IMP SITC 8: Miscellaneous manufactured articles
451IMP SITC 9: Commodities and transactions n.e.c.
452EXP SITC Total
453EXP SITC 0: Food and live animal
454EXP SITC 1: Beverages and tobacco
455EXP SITC 2: Crude materials, inedible, except fuels
456EXP SITC 3: Mineral fuels, lubricants and related materials
457EXP SITC 4: Animal and vegetable oils, fats and waxes
458EXP SITC 5: Chemicals and related products, n.e.s.
459EXP SITC 6: Manufactured goods
460EXP SITC 7: Machinery and transport equipment
461EXP SITC 8: Miscellaneous manufactured articles
462EXP SITC 9: Commodities and transactions n.e.c.
463NetherlandsIMP SITC Total
464Trade by productIMP SITC 0: Food and live animal
465IMP SITC 1: Beverages and tobacco
466IMP SITC 2: Crude materials, inedible, except fuels
467IMP SITC 3: Mineral fuels, lubricants and related materials
468IMP SITC 4: Animal and vegetable oils, fats and waxes
469IMP SITC 5: Chemicals and related products, n.e.s.
470IMP SITC 6: Manufactured goods
471IMP SITC 7: Machinery and transport equipment
472IMP SITC 8: Miscellaneous manufactured articles
473IMP SITC 9: Commodities and transactions n.e.c.
474EXP SITC Total
475EXP SITC 0: Food and live animal
476EXP SITC 1: Beverages and tobacco
477EXP SITC 2: Crude materials, inedible, except fuels
478EXP SITC 3: Mineral fuels, lubricants and related materials
479EXP SITC 4: Animal and vegetable oils, fats and waxes
480EXP SITC 5: Chemicals and related products, n.e.s.
481EXP SITC 6: Manufactured goods
482EXP SITC 7: Machinery and transport equipment
483EXP SITC 8: Miscellaneous manufactured articles
484EXP SITC 9: Commodities and transactions n.e.c.
485USAIMP SITC Total
486Trade by productIMP SITC 0: Food and live animal
487IMP SITC 1: Beverages and tobacco
488IMP SITC 2: Crude materials, inedible, except fuels
489IMP SITC 3: Mineral fuels, lubricants and related materials
490IMP SITC 4: Animal and vegetable oils, fats and waxes
491IMP SITC 5: Chemicals and related products, n.e.s.
492IMP SITC 6: Manufactured goods
493IMP SITC 7: Machinery and transport equipment
494IMP SITC 8: Miscellaneous manufactured articles
495IMP SITC 9: Commodities and transactions n.e.c.
496EXP SITC Total
497EXP SITC 0: Food and live animal
498EXP SITC 1: Beverages and tobacco
499EXP SITC 2: Crude materials, inedible, except fuels
500EXP SITC 3: Mineral fuels, lubricants and related materials
501EXP SITC 4: Animal and vegetable oils, fats and waxes
502EXP SITC 5: Chemicals and related products, n.e.s.
503EXP SITC 6: Manufactured goods
504EXP SITC 7: Machinery and transport equipment
505EXP SITC 8: Miscellaneous manufactured articles
506EXP SITC 9: Commodities and transactions n.e.c.
Annex II. France and Germany: Shocks to ULCM and TOT

France: Shocks to ULCM

France: Shocks to TOT

Germany: Shocks to ULCM

Germany: Shocks to TOT

References

Department of Economics, University of Johannesburg.

This paper was written while the author was a staff member of the European Department, IMF. Now, the author is at the Central Bank of Luxembourg.

*We thank participants at seminars at the MINEFI, France, ECB, and the European Department of the IMF for valuable comments on previous versions of this paper. Susan Becker made an efficient and dedicated management of the large databases used in this study. Any remaining errors or omissions are authors’ responsibility.

This model is closely related to the traditional factor models of Sargent and Sims (1977) and Geweke (1977), except that it admits the possibility of serial correlation and weakly cross-sectional correlation of idiosyncratic components, as in Chamberlain (1983) and Chamberlain and Rothschild (1983). Similar models have recently been used by Giannone, Reichlin, and Sala (2002), Forni and others (2005), and Eickmeier (2007).

See Appendix II for a technical description. See also Kabundi and Nadal De Simone (2007).

See Peersman (2005) for more technical details.

We are deeply grateful to Sandra Eickmeier for having provided us with the main code for the estimation and for her technical support and insights.

The identification of the shocks never required more than 530 draws.

VAR(1) provides a dynamic representation which is parsimonious and quite general (for more details, see Gianonne, 2005). The residuals ut were white noise and thus an autoregressive process of order 1 was chosen.

Uhlig (2003) shows that two shocks are sufficient to explain 90 percent of the variance at all horizons of real U.S. GNP.

If, for example, two orthogonal shocks are identified, it is incorrect to identify the first shock as the one corresponding to the first eigenvalue and the second orthogonal shock as the one corresponding to the second eigenvalue (see Uhlig, 2003). The two orthogonal shocks identified generate together the total variation which explanation is being maximized. However, there are multiple possible combinations of those orthogonal shocks all of which will still explain the total variation chosen: as an illustration, and measuring angles in degrees, the pairings of orthogonal shocks with rotation angles {0,90} or {30,120} or {60,150} would be equally acceptable. The grid of the angle of rotation can be different, of course. This paper uses a grid of 30 degrees.

See Peersman (2005) for more technical details.

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