Euro Area Policies: Selected Issues
Author: International Monetary Fund
Publication Date:
16 Sep 2003
eISBN:
9781451812947
Language:
English
Keywords:
ISCR; CR; price; euro area; producer price; output fluctuation; forecast error; impulse response function; Supply shocks; Industrial production; Deflation; Producer prices; Capacity utilization
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An important aim of this paper is to take shifts in the long-term anchor in the empirical specifications. The study examines exchange-rate pass-through and external adjustment in the euro area. The impact on third-country trade and investment is also discussed. A better understanding of the economic behavior underlying limited pass-through is an important consideration for investigating the implications of currency fluctuations and the pattern of external adjustment. The impulse-response patterns suggest a high degree of local currency pricing in import prices and producer currency pricing in export prices.

Abstract

An important aim of this paper is to take shifts in the long-term anchor in the empirical specifications. The study examines exchange-rate pass-through and external adjustment in the euro area. The impact on third-country trade and investment is also discussed. A better understanding of the economic behavior underlying limited pass-through is an important consideration for investigating the implications of currency fluctuations and the pattern of external adjustment. The impulse-response patterns suggest a high degree of local currency pricing in import prices and producer currency pricing in export prices.

II. Euro Area Business Cycles: The Role of Supply and Demand Disturbances1

A. Introduction

1. The identification and attribution of the sources of macroeconomic shocks have important implications in all areas of economic policy. If, for example, supply shocks are more important in the euro area than in the United States, this would argue for less activist demand management policies and more emphasis on structural policies. In particular, attempts to offset economic downturns that were due to negative supply shocks would have strong inflationary consequences.

2. The area’s more recent growth performance has been affected by an inordinate number of economic shocks. These have included, inter alia, the impact of the monetary union itself, the global equity market boom and bust, weather and disease related food disruptions, oil price shocks, and sharp movements in the exchange rate. An important question is whether, on balance, these shocks been mostly supply related, thereby generating a reduction in the area’s level of potential output? Or have they been predominantly demand-related shocks?

3. Attempts to identify the persistence of shocks have centered on the estimation of structural vector autoregressions (SVARs). Empirical research has focused on a few major euro-area countries and has tended to generate varying and sometimes conflicting results regarding the source of fluctuations and the degree of cross-country correlation of shocks. Moreover, apart from some recent research on the shock asymmetry of transition economies (relative to euro-area countries), there has been very little work on identifying shocks that affect the euro area aggregate economy.

4. Against this background, the purpose of this chapter is to examine the relative role of supply and demand shocks in driving macroeconomic fluctuations in the euro area. Section B takes stock of the existing literature on identifying supply and demand shocks in the euro area, with a particular focus on pinning down areas of agreement and disagreement. Section C describes the identification methodology using structural VARs. Section D introduces and analyzes the data. Section E presents the VAR results, and Section F concludes.

B. Literature on Identifying Supply and Demand Shocks in Europe

5. Most of the earlier literature focuses on individual EU countries. A number of authors using a variety of identification techniques, country datasets, and time periods have examined the dynamic behavior of output and prices in response to macroeconomic shocks (Table 1). Much of the work on shock dynamics took place in the early 1990s, as internal market integration was taking shape and developing, and as the list of possible early EMU entrants was being determined. At the same time, the imminent accession often transition countries to the European Union (EU), the need to continually assess the integration progress of current EMU members, and a number of sizable shocks which have recently hit the euro area, have rekindled interest in the nature of shocks in EU countries.

Table 1.

Summary of Research on Supply-Demand Sources of Euro-Area Economic Fluctuations

article image

Country abbreviations are as follows: Austria (A); Belgium (B); Canada (Can); Demark (D); Finland (Fl); France (F), Germany (DE); Iceland (IC); Ireland (Ir); Italy (I); Netherlands (NL); New Zealand (NZ); Norway (N); Switzerland (Sw); United Kingdom (U.K.); United States (U.S.). Euro area countries arc highlighted in bold.

Determination on which shocks arc the main driving factor behind output fluctuations.

6. A review of the literature in Table 1 suggests some preliminary conclusions:

  • A clear and authoritative answer on which type of shock, aggregate supply or aggregate demand, dominates in explaining the majority of the fluctuations in output has not been achieved. A slight majority of the studies, however, appears to point toward demand shocks as dominating at very short horizons.

  • Across countries, aggregate supply shocks appear to be positively correlated to those in Germany. However, there is very little evidence the correlation has increased over time, and it appears that across countries supply shocks may have become more correlated to French supply shocks over time, and less so to German ones.

  • Aggregate demand shocks appear to be smaller and less correlated than supply shocks across euro area countries.

  • Smaller periphery countries appear to face larger supply and demand shocks than core countries. At the same time, they also have more flexible wage and price systems that allow a less costly adjustment process.

C. The Blanchard-Quah (BQ) Structural VAR Methodology

7. The BQ methodology allows the identification of permanent and temporary structural shocks to a variable. This is achieved by imposing long-run restrictions on a VAR system while leaving short-run dynamics to be determined by the data. Assume the VAR model can be represented by an infinite moving average representation of a vector of variables xt, with an equivalent number of structural shocks εt:

Δxt = A0εt+A1εt−1+… = Σi=0∞Aiεt−i(1)

In this setup, the At matrices represent the impulse response functions of the shocks to the elements of x, while the ε vector contains the supply and demand shocks. When xt represents the changes of the logarithms of real output and prices, a more specific version of the model can be written as follows:

[ΔytΔpt] = Σi=0∞Li[a1Iia12ia1ia22i][εstεdt](2)

where

Var(εt) = Σ(3)

8. The fundamental shocks εsl and εdt are assumed to be orthogonal and therefore, the variance-covariance matrix Σ is diagonal. The BQ framework contains the restriction that supply shocks have permanent effects on the level of output while demand shocks have only temporary effects—implying that the cumulative effect of demand shocks on the change in output must be zero. Both shocks are allowed to have permanent effects on the level of prices. This restriction means that the matrix of long-run moving average coefficients, C(l) must be lower triangular:

Σi=0∞a11i = 0(4)

9. The structural VAR model defined by equations (2) and (4) can be estimated in its reduced form version by ordinary least squares. In typical VAR format, this means that each element of xt is regressed on lagged values of all the elements of x, with the estimated coefficients represented by B. That is:

xt = B1xt−1 + B2xt−2 + … + Bnxt−n + et(5)

where et represents residuals from the estimation of the reduced form VAR. Next, the following algebraic manipulation is used to find the matrix of long-run moving average coefficients:

xt = (I−B(L))−1et = (I+B(L)+B(L)2+…+)et(6)
xt = et+D1et−1+D2et−2+D3et−3+…(7)

10. To move back to the structural model given by equations (2) and (4), the residuals from the reduced form VAR, et, must be transformed into supply and demand shocks εt. This is accomplished by the restricted factor matrix C, such that et = Cεt. Given the two variable output growth and inflation case under consideration, four restrictions are required to define the four elements of C. Two of these restrictions are simple normalizations, which define the variance of the shocks εst and εdt. A third restriction comes from assuming that the supply and demand shocks are orthogonal. The final restriction regarding the temporary nature of demand shocks, uniquely defines the C matrix and implies equation (4) in the structural model. For the reduced form VAR, this means:

 = Σi=0∞[d11id12id21id22i][c11c12c21c22] = [..0....](8)

11. Although this restriction affects the response of output to the two shocks, it does not affect the impact of these shocks on prices. However, a basic aggregate supply (AS) and aggregate demand (AD) model (with a vertical long-run AS curve) implies that demand shocks should raise prices in both the short- and long-run, while supply shocks should lower prices. In this model, a positive demand shock will result in a shift of the AD curve to the right, and in the short-run, to higher output and prices (Figure 1). In the long-run, the output increase is short-lived as the price level increases to generate a new equilibrium output at potential along the new AD curve. A positive supply shock shifts short and long-run AS curves to the right by the same amount (Figure 2). Thus, in the short as well as in the long run, prices decline as output expands.

Figure 1:
Figure 1:

Demand Shock

Citation: IMF Staff Country Reports 2003, 298; 10.5089/9781451812947.002.A002

Figure 2:
Figure 2:

Supply Shock

Citation: IMF Staff Country Reports 2003, 298; 10.5089/9781451812947.002.A002

12. Since these responses are not imposed, authors who have used the BQ model to identify supply and demand shocks, have also examined the impulse response functions for these patterns as a form of over-identifying restriction. Researchers have found these useful in interpreting the results and ensuring that output and prices respond in a theoretically correct way to supply and demand shocks. The same type of identification check will be performed here as well and used as guide in the determination of correctly identified shocks.

D. Preliminary Data Analysis

13. Quarterly data on industrial production, real GDP, the GDP deflator, and consumer and producer prices were gathered for the euro area and for the United States. In addition, aggregates for the euro area (EA12), a “large country” version of the euro area comprised of Germany, France and Italy (EA3), and for the small periphery countries (EA9), were constructed for each of the variables. The maximum time span of the data runs from 1963:1 to 2002:3.

14. As a first step in the analysis, the stationarity properties of the logged data were examined using augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests for unit roots. In all cases an intercept and time trend were included in the tests. The results, presented in Table 2, suggest that in most instances, output series—both industrial production and real GDP—contain a unit root in levels but are stationary in their differences. Similarly, unit root tests on the producer price series imply that producer prices are stationary only in their logged first differences.

Table 2.

Unit Root Tests 1/

(1963:1 to 2002:3)

article image
Source: Staff estimates.

Augumented Dicky-Fuller (ADF) and Phillips-Perron (PP) tests with constant and time trend were calculated over the full sample (1963:1-2002:3). The 1, 5 and 10 percent critical values are -4.018, -3.439, and -3.143, respectively. The null hypothesis is that the level series contain a unit root. Rejections of the null hypothesis at the 5 percent level have been put in bold.

15. The results for real GDP deflator and CPI, however, indicate that in many countries these price series may remain non-stationary in their growth rates, (i.e., implying 1(2) behavior), making their use in a bivariate VAR analysis problematic. Since all of the price series considered here are very much interrelated, one could assume that similar unit root properties exist, and given the low power of these tests, proceed under the assumption that the data generating process of prices is 1(1). However, exploratory examination of the price impulse response functions (in the context of the VAR analysis) using these unadjusted GDP deflators and CPI series created problems in shock identification. Thus, these series were further tested for possible mean shifts and trend breaks, which can create the illusion of 1(2)-type behavior. The results (not presented here) indicated that after adjusting for deterministic mean shifts and trend breaks using Perron’s (1997) technique, both the CPI and GDP deflator inflation series could be considered stationary and usable in the next stage of the analysis.

E. Empirical Results of the Structural VAR Analysis

16. A bivariate VAR model was estimated and structural shocks identified as discussed above. The number of lags was set to four since the Schwartz Bayesian information criterion indicated that all the models had an optimal lag length of either three or four. A uniform lag structure was chosen to allow comparisons across countries. In the analysis below, all shocks were normalized to a unit shock to the system. The impulse response function analysis using real GDP and GDP or CPI deflators on the full sample indicated that while the initial output response to positive demand shocks would be positive as expected, it would quickly turn negative before gradually dissipating, suggesting a problem with the identification restrictions. Therefore, the analysis below focuses on 3 output-price pairs—industrial production and producer prices for both sample periods, and on real GDP and the GDP deflator for the period 1980:1 to 2002:3.

Impulse response functions

17. Figures 3-5 present the impulse response results for the euro area and the United States. In all instances, the estimation and simulation results are in line with the AS-AD framework discussed in Section C. That is, positive aggregate demand shocks are associated with increases in prices while aggregate supply shocks are associated with declines in prices. Also, the BQ restriction is reflected by the temporary effects of aggregate demand shocks on the level of output, while aggregate supply shocks have permanent effects.

Figure 3.
Figure 3.

Impulse Response Functions for the Euro Area and U.S.

(Industrial Production and Producer Prices; 1963.1-2002.3)

Citation: IMF Staff Country Reports 2003, 298; 10.5089/9781451812947.002.A002

Sources: ECB; Eurostat; IMF; and own calculations.
Figure 4.
Figure 4.

Impulse Response Functions for the Euro Area and U.S.

(Industrial Production and Producer Prices; 1980.1-2002.3)

Citation: IMF Staff Country Reports 2003, 298; 10.5089/9781451812947.002.A002

Sources: ECB; Eurostat; IMF; and own calculations.
Figure 5.
Figure 5.

Impulse Response Functions for the Euro Area and U.S.

(Real GDP and GDP Deflator; 1980.1-2002.3)

Citation: IMF Staff Country Reports 2003, 298; 10.5089/9781451812947.002.A002

Sources: ECB; Euruslal; IMF; and own calculations.

18. A number of results can be distilled from these impulse response functions. First, in most cases, demand shocks appear to be more, or at least as important, for output as supply shocks in the short run. This is especially the case for the euro area using industrial production. Second, in general, disturbances appear to have more protracted effects in the euro area than in United States. For example, the impulse response function for the euro area using industrial production indicates that demand disturbances have large effects that peak within 4 quarters and then decline, leveling off at around 0.2 after 12 quarters but do not fully vanish until some 5-6 years. In the United States, the effects of demand shocks on output vanish after a little over two years. Third, the magnitude of the shocks on output differs between the United States and the euro area aggregates. For example, output effects from demand shocks in the first year appear to be about 50 percent larger in the euro area than in the United States when using industrial production. Supply shocks on output appear to be of equal size in the United States and the euro area. However, some of these differences may be the result of aggregation issues. In sum, while these impulse response function results are similar to Bayoumi and Eichengreen (1992a) and others, they do seem to indicate that the size of aggregate demand shocks in Europe may have increased.

Forecast error variance decompositions

19. Table 3 contains the results of the output forecast error variance decompositions for the 3 output-price pairs; the table also reports results for the EA3 and EA9 aggregates. The numbers reveal the percentage of forecast errors that can be attributed to supply innovations at eight different forecast horizons: one to two quarters ahead (short-run); four to twelve quarters ahead (medium-run); and 20 to 36 quarters ahead (long-run). These forecast error variance decompositions (and impulse response functions) indicate the significance of the different shocks on average over the entire sample period.

Table 3.

Forecast Error Variance Decomposition of Output 1/

article image
Source: Staff estimates.

Since supply and demand shock contributions add up to 100 percent, 100- minus these supply contributions represent the demand contribution. By definition of the BQ identification, the supply shock contribution must asymptotically go to 100 percent in the long-run.

20. For the period covering the last forty years, the results indicate that demand innovations arc dominant in the very short-run in the euro area when using industrial production as a proxy for output. For the United States, the results suggest that demand innovations have a very short-run impact, with supply innovations by the second quarter explaining over 50 percent of output variance, again suggesting a relatively fast reaction to demand shocks.

21. Given the demand side pressures of the 1960s and the oil price shocks of the 1970s, an important question is how these results would change if the sample was limited to the 1980-2002 experience. In essence, those more turbulent periods may be dominating the overall outcomes, and thus their elimination should allow a better understanding of how the more recent shocks have been affecting output. The middle panel of Table 3 presents the forecast error variance for aggregates using industrial production and producer prices, but with the shorter sample period. The results imply a stronger dominance of supply shocks—above the 50 percent threshold—in determining output fluctuations at all horizons. This outcome indicates that sensitivity to sample period may be driving the different findings found in the literature on which shock dominates output fluctuations. The bottom panel of Table 3 contains the results of using real GDP and the GDP deflator from 1980:1 to 2002:3. Here, the data aggregation issue appears to have a greater impact; demand innovations explain a sizable amount of output fluctuations at a short horizon for the EA12 aggregate, while the results for EA3 aggregate imply the opposite.

Historical decompositions: a more detailed look at the most recent experience

22. Using the estimates from the VAR model, it is also possible to calculate historical decompositions which measure the unconditional forecast error for each of the variables. This forecast is defined as the difference between the realized level of the variable and the unconditional forecast from the deterministic component of the VAR. Then the forecast errors in the level of each variable can be decomposed into components attributable to each of the shocks. Given our goal of identifying the most recent shock experience since the start of EMU, the focus will be on the decomposition of output forecast errors leading up to and since 1999.

23. Figures 6 through 8 contain the decompositions for EA12 and EA9 aggregates as well as for the United States. Figure 6 shows the results using industrial production for the full sample period. For the euro area aggregates, a majority of output total forecast errors (tfe) in this period have been driven by demand shocks—in line with the previous evidence presented above. In the United States, demand innovations play an important role, but it is interesting to see that the build up and magnitude of the supply innovations that drove output since the mid-1990s.

Figure 6.
Figure 6.

Decomposition of Output Total Forecast Error 1/

(Industrial Production and Producer Prices: 1963:1 to 2002:3)

Citation: IMF Staff Country Reports 2003, 298; 10.5089/9781451812947.002.A002

Source: Staff estimates.1/ Total forecast errors are based on an 4-quarter ahead forecast. To reduced the volatility of the figures, a two quarter average of the components are shown. The summation of supply and demand components equal the total forecast error.

24. Focusing on the 1999-2002 period only, for the euro area aggregates, supply innovations have played a role, remaining mostly negative since 2000. However, this particular decomposition clearly indicates that negative demand innovations were the main reason for the slowing in output growth, especially since mid-2001. Regarding the United States, the results indicate that negative supply innovations played a relatively minor role in reducing output growth.

25. The forecast error variance decompositions (Table 3) suggested that using the shorter sample period 1980-2002 markedly increases the dominance of supply shocks. Would a historical decomposition of recent output forecast errors attribute most of the variation to supply side innovations as well? To answer this question, the historical decomposition analysis was run using the shorter sample (Figure 7). Interestingly, the historical decomposition of the total forecast error for the euro area aggregates still tend to place greater emphasis on the demand disturbances. In the United States, however, there would appear to be more of an even mix of supply and demand side influences.

Figure 7.
Figure 7.

Decomposition of Output Total Forecast Error 1/

(Industrial Production and Producer Prices: 1980:1 to 2002:3)

Citation: IMF Staff Country Reports 2003, 298; 10.5089/9781451812947.002.A002

Source: Staff estimates.1/ Total forecast errors are based on an 4-quarter ahead forecast. To reduced the volatility of the figures, a two quarter average of the components are shown. The summation of supply and demand components equal the total forecast error.

26. Would the use of the real GDP and GDP deflator pair change the analysis? Figure 8 indicates that the use of these series would place more weight on a mix of both demand and supply innovations in determining forecast errors. For the euro area, demand innovations first turn sharply downward in mid-2000, with supply shocks moving markedly downward about a year later. Moreover, negative supply shocks continued to impact output at the end of 2002. For the United States, the results also indicates that a mix of negative supply and demand factors was at work in downswing. But, in contrast to the euro area, there is a sharp rebound in the supply shock component at the end of the sample period.

Figure 8.
Figure 8.

Decomposition of Output Total Forecast Error 1/

(RGDP and GDP Deflator: 1980:1 to 2002:3)

Citation: IMF Staff Country Reports 2003, 298; 10.5089/9781451812947.002.A002

Source: Staff estimates.1/ Total forecast errors are based on an 4-quarter ahead forecast. To reduced the volatility of the figures, a two quarter average of the components are shown. The summation of supply and demand components equal the total forecast error.

27. One troubling aspect of the total forecast error historical decompositions using industrial production is the appearance of positive supply shocks in the middle of the downturn. This may be due to the large swings in producer prices during this period. In essence, the identification procedure may have taken the downturn in producer prices or return to its mean as a signal of a positive supply shock. And since output was falling, demand innovations may have been erroneously identified as the main driving factor—or at least given it too much weight.

28. To try to avoid this problem, bivariate VAR decompositions were also estimated using industrial production and capacity utilization. Given the inherent stationarity of capacity utilization, its use would be more in line with the original implementation of the BQ methodology, which used the logged difference of U.S. output and the level of unemployment. Capacity utilization data are also often based on survey data, and therefore incorporates information on actual perceptions of economic slack in the economy. At the same time, the use of capacity utilization has drawbacks. First, long capacity utilization series are only available for the EA3 and the United States. Second, the impulse response function for positive supply shocks to capacity utilization tended to report an increase in utilization, perhaps suggesting problems with the identification restrictions.

29. Figure 9 presents the historical decompositions of output forecast errors using industrial production and capacity utilization. For the euro area, this particular decomposition suggests some evidence of a short positive supply buildup in 1996-98 and attributes most of the 2000 output expansion to supply-side factors. In addition, the sharp slowdown in output starting in the second half of 2000 is attributed to a mixture of both supply and demand shocks. By comparison, the results for the United States place even more emphasis on supply side innovations, implying that the majority of the slowdown starting in 2000 in the United States can be linked to supply side factors.

Figure 9.
Figure 9.

Decomposition of Output Total Forecast Error 1/

(Capacity Utilization and Producer Prices:1985:l to 2002:3)

Citation: IMF Staff Country Reports 2003, 298; 10.5089/9781451812947.002.A002

Source: Staff estimates.1/ Total forecast errors are based on an 4-quarter ahead forecast. To reduced the volatility of the figures, a two quarter average of the components are shown. The summation of supply and demand components equal the total forecast error.

F. Conclusions

30. The results of this empirical identification of the shocks that have hit the euro area provide the basis for some tentative conclusions:

  • As to the question of which innovations—supply or demand—explain output fluctuations on average, the answer is very sensitive to the period examined and the data set used. Using a long sample, in the euro area demand innovations seem to dominate, on average, the explanation of output fluctuations at the short-to-medium horizon.

  • If, however, one abstracts from the turbulent 60-70s period and focuses only on the 1980s-2002 period, supply side factors would appear to play a more dominate role in explaining output fluctuations (as defined by industrial production) on average. This is especially so in the euro area, which under the shorter sample period reports small demand side influences at the very short forecast horizon. However, the use of real GDP and the GDP deflator indicates that the euro area output fluctuations could be dominated by demand innovations even using a shorter sample period.

  • The results of historical decompositions (using industrial production and producer prices) that attribute forecast errors in any one period to supply or demand innovations indicate that demand disturbances may have played the main role in driving the current downturn in the euro area and the United States. The fact that this result was found using both the long and short sample periods indicates some degree of robustness.

  • The outcome of historical decompositions using real GDP and the GDP deflator indicates that demand factors played at least a substantial role in the current downturn in the euro area and in the United States. However, negative supply innovations also played a decisive role. Moreover, for the euro area, it appears these negative factors, particularly on the supply side, are still at work.

  • Historical decompositions using industrial production and capacity utilization, by contrast, suggest that supply factors played a dominant role in the current downturn, both in the euro area and the United States. The apparent discrepancy in the results between the decompositions that use prices compared with the decompositions that use capacity utilization should be a subject for further research.

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Prepared by Kevin Ross.

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Cover IMF Staff Country Reports
Euro Area Policies: Selected Issues
Author: International Monetary Fund
Volume/Issue:
Volume 2003: Issue 298
Publisher:
International Monetary Fund
ISBN:
9781451812947
ISSN:
1934-7685
Pages:
109
DOI:
https://doi.org/10.5089/9781451812947.002
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    Demand Shock

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    Supply Shock

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    Impulse Response Functions for the Euro Area and U.S.

    (Industrial Production and Producer Prices; 1963.1-2002.3)

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    Impulse Response Functions for the Euro Area and U.S.

    (Industrial Production and Producer Prices; 1980.1-2002.3)

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    Impulse Response Functions for the Euro Area and U.S.

    (Real GDP and GDP Deflator; 1980.1-2002.3)

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    Decomposition of Output Total Forecast Error 1/

    (Industrial Production and Producer Prices: 1963:1 to 2002:3)

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    Decomposition of Output Total Forecast Error 1/

    (Industrial Production and Producer Prices: 1980:1 to 2002:3)

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    Decomposition of Output Total Forecast Error 1/

    (RGDP and GDP Deflator: 1980:1 to 2002:3)

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    Decomposition of Output Total Forecast Error 1/

    (Capacity Utilization and Producer Prices:1985:l to 2002:3)

Demand Shock

Supply Shock

Impulse Response Functions for the Euro Area and U.S.

(Industrial Production and Producer Prices; 1963.1-2002.3)

Impulse Response Functions for the Euro Area and U.S.

(Industrial Production and Producer Prices; 1980.1-2002.3)

Impulse Response Functions for the Euro Area and U.S.

(Real GDP and GDP Deflator; 1980.1-2002.3)

Decomposition of Output Total Forecast Error 1/

(Industrial Production and Producer Prices: 1963:1 to 2002:3)

Decomposition of Output Total Forecast Error 1/

(Industrial Production and Producer Prices: 1980:1 to 2002:3)

Decomposition of Output Total Forecast Error 1/

(RGDP and GDP Deflator: 1980:1 to 2002:3)

Decomposition of Output Total Forecast Error 1/

(Capacity Utilization and Producer Prices:1985:l to 2002:3)