France
Selected Issues
Author:
International Monetary Fund
Search for other papers by International Monetary Fund in
Current site
Google Scholar
Close

The export performance of the French economy relative to its own past and relative to a major trading partner, Germany, has deteriorated. The risk analysis indicates that French firms have seen a significant improvement in the corporate health, and seem resilient to the recent financial shock despite differences across firms. Several issues in the context of common EU tax policy formation, including carbon pricing, control problems associated with the zero-rating of intra-EU supplies, and possible movement toward a common corporate tax base need to be addressed.

Abstract

The export performance of the French economy relative to its own past and relative to a major trading partner, Germany, has deteriorated. The risk analysis indicates that French firms have seen a significant improvement in the corporate health, and seem resilient to the recent financial shock despite differences across firms. Several issues in the context of common EU tax policy formation, including carbon pricing, control problems associated with the zero-rating of intra-EU supplies, and possible movement toward a common corporate tax base need to be addressed.

I. Recent French Export Performance: Is There A Competitiveness Problem?1

A. Introduction

1. A significant part of French activity fluctuations has a foreign source (e.g., Kose, Otrok, and Whiteman, 2003, Kabundi and Nadal De Simone, 2007).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 growth of emerging Asian economies and the eastward expansion of the 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.2

2. 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.

3. 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 U.S. in terms of level, TFP growth is hampered by the high ratio of minimum to median wages.3 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.4

4. This paper performs a descriptive analysis of French export data by destination and by SITC Revision 3 product classification distinguishing (optimally) 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. In particular, the paper contrasts and compares the reaction of French and German variables to shocks to unit labor costs in manufacturing and to terms of trade.

5. 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 U.S.

6. There is a consensus in the literature that globalization has positive effects. Globalization fosters comovement of macroeconomic variables across countries through trade and financial market integration (Imbs, 2004). Financial market integration has also contributed to the synchronization of business cycles through the opening of countries’ capital accounts. Financial prices have become more synchronized through arbitrage (Brooks, Forbes, and Mody, 2003).

7. On the empirical front, most findings show increasing synchronization of economic variables across countries (Nadal De Simone, 2002, Bordo and Helbling, 2004, Kose, Otrok, and Whiteman, 2005).Alternatively, despite large increases in trade and financial openness, G-7 business cycles may have become less synchronized as a result, for instance, that trade flows lead to increased specialization of production (Stock and Watson, 2003, Kose and Yi, 2006).

8. In addition, other studies have emphasized the sources of shocks, their spillovers, and channels of their transmission from one country or region to another. Recent examples include the study of the monetary transmission mechanism in the euro area using structural VAR analysis by Ciccarelli and Rebucci (2006), and Canova, Ciccarelli, and Ortega (2007). 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. Given that the VAR methodology has some limitations—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 (2002) use the approximate structural dynamic factor model on a large panel of developed countries’ variables and, like Kabundi and Nadal De Simone (2007) and Eickmeier (2007), find that U.S. demand shocks and EU supply shocks have a positive and significant effect on French and German output.

9. The high degree of economic and financial integration 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.

10. 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 euro appreciation. (5) However, idiosyncratic factors were negative on average for France and positive for Germany; that was especially the case vis-a-vis China and Asia, and notably in terms of food and live animal, beverages and tobacco, and manufacturing. (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. In France, 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.

B. Data and Non Stationarity

Data

11. This study uses two large data panels.The first one comprises 396 quarterly macroeconomic series and 106 series of trade by country (for a total of series N = 502). Trade series include imports and exports to the euro area, the EU, accession 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 (i.e., T = 104). The countries are France, Germany, Japan, the Netherlands, the United Kingdom, and the United States. In addition, 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. Variables have no seasonal component left.

Dealing with non stationarity

12. 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 (Corbae and Ouliaris, 2006). There are several reasons for this approach. First, 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, first differencing removes a significant part of the variance of economic time series. Third, Corbae and Ouliaris filter is consistent, is not subject to end-point problems and has no finite sampling error. As an illustration, 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)

uA01fig01

France: Spectra of Real GDP Filtered

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

Citation: IMF Staff Country Reports 2008, 074; 10.5089/9781451813708.002.A001

C. Descriptive Part: Facts Without the Noise

13. Several interesting features of recent French export performance are clear from the data once the noise of short-term fluctuations is removed. First, French and German cyclical components of exports by country and products follow the same pattern, mimicking quite closely their business cycles. 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 have a higher short-term elasticity. Hence, divergences in recent trade performance between both countries seem unrelated to the cyclical part of trade flows. What about their trend part? Figure I-1 shows annual exports trend growth. 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 was also weak relative to Germany in terms of exports to the EU, the euro area, the U.S., and the U.K. France’s export performance is also weaker relative to its own past (Table I-1). Furthermore, France’s trend export growth rate in the 2000s, while higher than in the 1990s, was less than 60 percent of Germany’s.

Figure I-1.
Figure I-1.
Figure I-1.

Trend Exports from France and Germany by Destination

Citation: IMF Staff Country Reports 2008, 074; 10.5089/9781451813708.002.A001

Table I-1.

Trend Exports per Region

(Average annual percent change)

article image

14. 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 on SITC categories 1, 3, 4, 5 and 7 (i.e., 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.

15. The analysis suggests that there has been since 2002-03 a clear underperformance of French exports relative to the past and relative to Germany. 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. That France is less competitive in recent years does not seem to be related to the euro; its underperformance is quite broad from a product viewpoint. Yet, more analysis and time is needed to conclude that there is a structural issue.

D. Analytical Part: ULCM and TOT Shocks

The model and economic conditions for shocks identification

16. To gain further insight into the causes of the deteriorating performance of the French foreign sector, this study uses a large dimensional approximate dynamic factor model in the tradition of Stock and Watson (2002). 5 The estimation of the model comprises two main steps: estimating the common components and identifying a reduced number of structural shocks that explain the common components of the variables of interest.6 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 features: (1) maximize the explained variance of the forecast error of the chosen variable and calculate impulse-response functions; (2) assume that identified shocks are linearly correlated to a vector of fundamentals; and (3) identify orthogonal shocks by rotation using a sign-identification strategy that imposes inequality restrictions on the impulse-response functions of variables based on a typical aggregate demand and aggregate supply framework.7 Only those rotations among all possible rotations that have a structural meaning are chosen.

17. The choice of the variables of interest was motivated by two observations. First, France’s economic activity is largely influenced by world developments. Thus, it seemed natural to identify a terms of trade (TOT) shock to compare the behavior of France relative to Germany. Second, only using unit labor cost measures of the REER, it 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, only partially compensated by higher productivity growth. Thus, the second shock identified was a shock to unit labor costs in manufacturing (ULCM). The choice of shocks seems also relevant given last section results. The text table displays the sign restrictions for shock identification, imposed contemporaneously and during the first year after the shock.

article image

18. 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.

Estimation

19. 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.8 Using Bai and Ng’s (2002) selection criteria, four factors were retained. 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. 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, the same analysis done on a data set containing only French variables shows that the resulting impulse-responses are similar to those of the broader data set, supporting the identification of shocks’ source.

20. 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. As noted above, 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. 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.

E. Econometric Results

21. Results are presented in the form of variance decomposition.9 Tables I-3 to I-6 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 and TOT, respectively. These shocks suffice to explain up to 99 percent and to about 98 percent of the error variance of the common components of French and German ULCM and 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. This suggests that France’s TOT are more influenced by idiosyncratic factors than Germany’s. As in Kabundi and Nadal De Simone (2007), the TOT are relatively less significant channels of shock transmission for France.

Table I-2.

Trend Exports per Product SITC

(Average annual percent change)

article image
Table I-3.

Forecast Error Variance of the Common Components of Germany Variables Explained by the Supply and Demand Shock to ULCM, 1981–2006 1/

article image

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

Table I-4.

Forecast Error Variance of the Common Components of Germany Variables Explained by the Supply and Demand Shock to ULCM, 1981–2006 1/

article image

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

Table I-5.

Forecast Error Variance of the Common Components of France Variables Explained by the Supply and Demand Shock to TOT, 1981–2006 1/

article image

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

Table I-6.

Forecast Error Variance of the Common Components of Germany Variables Explained by the Supply and Demand Shock to TOT, 1981–2006 1/

article image

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

22. 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 processes are significantly different.

23. In both countries, supply shocks to ULCM reduce output, private consumption, investment, and the volume of exports. 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 negative effect on output, 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 rises somewhat despite the fall in labor productivity. The dollar value of exports to all destinations increases in Germany, but not in France (e.g., exports to the U.S. fall). The total increase in the dollar value of French exports is half that of German exports. The same results apply 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. France adjusts relatively less via price and wage changes, and more via employment changes than Germany.

24. Demand shocks to ULCM affect France and Germany differently. In France, a demand shock to ULCM 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 rise is short lived and gets undone already after 1½ years. Exports volume decreases 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.

25. TOT shocks affect France less than Germany; 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. 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 relatively longer to reach that level in France. 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; the fall is more pronounced in Germany due to the larger short-run exchange rate depreciation. Exports by product in euros show no major clear patterns, but there is in general a slight increase. Summarizing, supply shocks that increase the TOT are more consistent with a persistent supply shock in Germany than in France.

26. An upward, demand-driven shock to TOT results in a negative output effect in France and is 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 U.S. and to accession countries. The increase is, however, larger for Germany. The euro value of French exports increases less than German exports. In fact, France’s exports are mostly flat, except for crude materials, animal and vegetable oils, chemicals, and commodities and transactions n.e.c. Overall, France adapts less quickly to inflationary pressures due to strong world demand.

F. Conclusion and Policy Implications

27. French economic activity is significantly affected by economic activity in the rest of the world. In recent years, the export performance of the French economy relative to 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. This study has found that the recent deterioration of French export performance does not seem to be related to the “cycle,” but to the trend growth of exports, which seems lower in the early 2000s than it was in the past and with respect to Germany. This result applies to exports by destination and by product composition.

28. The analysis of the effects of an increase in ULCM and in the TOT, suggests that the French economy is relatively less flexible to adjust than the German economy. Faced with an upward shift in ULCM, France adjusts relatively less via price and wage changes, and more via employment changes than Germany. The same differences are also evident when both countries are faced with an upward TOT shock. Given that the convergence of the SMIC operated between 2003 and 2006 represented a significant increase in unit labor costs in the economy, 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.

29. 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 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 a given disturbance. In addition, the analysis highlights the importance of measures that increase productivity and, in particular, the desirability of avoiding SMIC adjustments unrelated to changes in productivity.

References

  • Bai, J., and S. Ng., 2002, “Determining the Number of Factors in Approximate Factor Models” Econometrica, 70(1), pp. 191 -221

  • Bordo, M.D., and T. F. Helbling, 2004, “Have National Business Cycles become more synchronized?” in Siebert, Horst (ed.) Macroeconomic Policies in the World Economy, Springer Verlag (Berlin and Heidelberg).

    • Search Google Scholar
    • Export Citation
  • Brooks, R., K. Forbes, and A., Mody, 2003, “How strong are Global Linkages?”, Manuscript, http://www.imf.org/external/np/res/seminars/2003/global/pdf/over.pdf.

    • Search Google Scholar
    • Export Citation
  • Canova, F., and M., Ciccarelli, 2006, “Estimating Multi-Country VAR Models,” ECB Working Paper 603.

  • Canova, F., and E. Ortega, 2007, “Similarities and Convergence in G-7 Countries,” Journal of Monetary Economics, 53(3), pp. 850-878.

    • Search Google Scholar
    • Export Citation
  • Canova, F., and G. de Nicoló, 2003, “On the Sources of Business Cycles in the G-7,” Journal of International Economics, 46, pp. 133-66.

    • Search Google Scholar
    • Export Citation
  • Chamberlain, G., 1983, “Funds, Factors, and Diversification in Arbitrage Pricing Models,” Econometrica 51, pp. 1281-304.

  • Ciccarelli, M. and A. Rebucci, 2006, “Has the transmission mechanism of European monetary policy changed in the run-up to EMU?” European Economic Review, 50(3), pp. 737-776.

    • Search Google Scholar
    • Export Citation
  • Corbae, D., and Ouliaris, S., 2006, “Extracting Cycles from Nonstationary Data,” Econometric Theory and Practice: Frontiers of Analysis and Applied Research, Cambridge University Press, D. Corbae, S. Durlauf and B. Hansen, eds.

    • Search Google Scholar
    • Export Citation
  • Eickmeier, S., 2007, “Business Cycle Transmission from the U.S. to Germany—A Structural Factor Approach,” European Economic Review, 51, pp. 521-551.

    • Search Google Scholar
    • Export Citation
  • Eickmeier, S. and J. Breitung, 2006, “How synchronized are new EU member states with the euro area? Evidence from a structural factor model,” Journal of Comparative Economics, Vol. 34(3), pp. 538-563.

    • Search Google Scholar
    • Export Citation
  • Forni, M., D. Giannone, M. Lippi, and L. Reichlin, 2005, “Opening the Black Box: Structural Factor Models with Large Cross-Sections,” manuscript.

    • Search Google Scholar
    • Export Citation
  • Geweke, J., 1977, “The Dynamic Factor Analysis of Economic Time Series,” inLatent Variables in Socio-Economic Models; edited by D.J. Aigner and A.S. Golberger (Amsterdam: -Holland), p.19

    • Search Google Scholar
    • Export Citation
  • Giannone, D., L. Reichlin, and L. Sala, 2002, “Tracking Greenspan: Systematic and Unsystematic Monetary Policy Revised,” CEPR Working Paper 3550.

    • Search Google Scholar
    • Export Citation
  • Imbs, J., 2004, “Trade, Finance, Specialization and Synchronization,” Review of Economics and Statistics, 86(3), pp. 723-34.

  • IMF, 2005, “France, Germany, Italy, and Spain: Explaining Differences in External Sector Performance Among Large Euro Area Countries,” IMF Country Report No. 05/401 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Kabundi, A. Nadal F. De Simone, 2007, “France in the Global Economy: A Structural Approximate Dynamic Factor Model Analysis,” IMF Working Paper 07/129 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Khan, T., 2006, “Productivity Growth, Technological Convergence, R&D, Trade, and Labor Markets: Evidence from the French Manufacturing Sector,” IMF Working Paper 06/230 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Kose, M.A., C. Otrok, and C.H. Whiteman, 2003, “International Business Cycles: World, Region, and Country-Specific Factors,” American Economic Review, 93(4), pp. 1216-39.

    • Search Google Scholar
    • Export Citation
  • Kose, M.A., C. Otrok, and C.H. Whiteman, 2005, “Understanding the Evolution of World Business Cycles,” IMF Working Paper 05/211 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Kose, M.A. and K. Yi, 2006, “Can the Standard International Business Model Explain the Relation between Trade and Comovement?” Journal of International Economics, 68, pp. 267-295.

    • Search Google Scholar
    • Export Citation
  • Nadal De Simone, F., 2002, “Common and Idiosyncratic Components in Real Output: Further International Evidence,” IMF Working Paper 02/229 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Peersman, G, 2005, “What Caused the Early Millennium Slowdown? Evidence Based on Vector Autoregressions,” Journal of Applied Econometrics, 20, pp. 185-207.

    • Search Google Scholar
    • Export Citation
  • Sargent, T.J., and C.A. Sims, 1977, “Business Cycle Modeling without Pretending to have too Much a Priori Economic Theory,” in New Methods in Business Research, edited by C.A. Sims (Minneapolis: Federal Reserve Bank of Minneapolis).

    • Search Google Scholar
    • Export Citation
  • Sims, C.A. and T. Zha 1999, “Error Bands for Impulse Responses,” Econometrica 67(5), pp. 1113-55.

  • Stock, J.H., and M.H. Watson, 2002, “Macroeconomic Forecasting Using Diffusion Indexes,” Journal of Business & Economic Statistics, 20(2), pp. 147-162.

    • Search Google Scholar
    • Export Citation
  • Stock, J.H., 2003, “Has the Business Cycle Changed? Evidence and Explanations,” in Monetary Policy and Uncertainty, Federal Reserve Bank of Kansas City, pp. 9-56.

    • Search Google Scholar
    • Export Citation
  • Uhlig, H., 2003, “What Moves Real GNP?” manuscript.

1

Prepared by A. Kabundi (University of Johannesburg) and F. Nadal De Simone.

2

See IMF, 2005.

5

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

6

See Kabundi and Nadal De Simone (2007) for a description of the model.

7

See Peersman (2005) for more technical details.

8

We are grateful to Sandra Eickmeier for having provided the main code and for her insights.

9

Refer to Kabundi and Nadal De Simone, IMF Working Paper, forthcoming, for impulse-response analysis.

  • Collapse
  • Expand
France: Selected Issues
Author:
International Monetary Fund
  • France: Spectra of Real GDP Filtered

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

  • Figure I-1.

    Trend Exports from France and Germany by Destination