This Selected Issues paper estimates a dynamic model of foreign currency loans to households in Austria to analyze their behavior and assess the effectiveness of measures intended to stem their rise. This paper also studies the developments in Austria’s economic linkages with Germany and the Central and Eastern European countries (CEECs). It finds that there has been delinking from Germany, albeit measured, while economic relationships with key CEEC trading partners have become stronger. The paper also discusses the dynamics of Austria’s economic linkages with Germany, and examines these linkages with the CEECs.

Abstract

This Selected Issues paper estimates a dynamic model of foreign currency loans to households in Austria to analyze their behavior and assess the effectiveness of measures intended to stem their rise. This paper also studies the developments in Austria’s economic linkages with Germany and the Central and Eastern European countries (CEECs). It finds that there has been delinking from Germany, albeit measured, while economic relationships with key CEEC trading partners have become stronger. The paper also discusses the dynamics of Austria’s economic linkages with Germany, and examines these linkages with the CEECs.

II. Austrian Economic Growth and the Linkages to Germany and Central and Eastern Europe31

A. Introduction

59. Situated at the heart of the European continent, Austria benefits from access to diverse markets, including mature and emerging-market economies. In particular, Austria’s strong economic ties to Germany over the years have helped sustain a relatively stable growth path and provide a buffer against external shocks. Signs are emerging, however, that the Austrian economy is gradually becoming less dependent on Germany, while its links with the faster-growing economies of the Central and Eastern European countries (CEECs)32 are becoming stronger. Austria’s output growth rates averaged 2.3 percent annually over the past decade, in line with euro area economies (see Figure 1). However, in recent years, Austria’s real GDP has outperformed other euro area countries. For example, between 2002 and 2004, Austria’s real GDP rose, on average, by 1.4 percent, compared with Germany, Italy, and the euro area’s growth rates of 0.5, 0.6, and 1.1 percent, respectively.33

Figure 1.
Figure 1.

Austria, Germany, and Euro Zone: Real GDP Growth, 1994-2004

(In percent)

Citation: IMF Staff Country Reports 2005, 249; 10.5089/9781451802375.002.A002

Sources: IMF, World Economic Outlook.

60. This paper studies the developments in Austria’s economic linkages with Germany and the CEECs. It finds that there has been delinking from Germany, albeit measured, while economic relationships with key CEEC trading partners have become stronger. Section B discusses the dynamics of Austria’s economic linkages with Germany, while Section C examines these linkages with the CEECs. The application of a gravity model in Section D investigates whether the trend in Austria’s trade intensity with Germany and the CEECs is consistent with model-based predictions. Section E offers concluding remarks.

B. The Austrian-German Connection

61. For decades, developments in the Austrian economy have been closely associated with economic conditions in Germany, particularly on the trade side. Austria had much to gain from its close proximity to a large, prosperous economy. Between 1983 and 2004, Austria’s exports to Germany more than tripled in constant U.S. dollar terms, and in 2004 they accounted for just over 12 percent of Austria’s GDP—roughly double the rate in 1983. In 2004, Germany remained Austria’s largest trading partner by far, responsible for about 31 percent of Austria’s total world exports.34

62. Earlier academic studies found evidence of a close relationship between the German and Austrian business cycles. For example, Cheung and Westermann (2000) employed various time-series techniques to examine the interactions between the two countries’ industrial production for the period 1962-94 and found evidence that the German economy exerted strong influences on the Austrian economy. Winckler (1993) showed that annual Austrian and German output growth rates were correlated at different lags, a result that was later confirmed by Cheung and Westermann (1999) using a bivariate error-correction model. While not concentrating solely on the Austria-Germany relationship, Fidrmuc and Korhonen (2003) analyzed the similarity of supply-and-demand shocks within the euro area and found that shocks in euro area countries were quite highly correlated.

63. However, Austria’s economic linkages with Germany are relatively weaker today. Austria continues to enjoy increased exports to Germany in U.S. dollar terms. But, as a fraction of total exports, the share of Austria’s exports to Germany has been trending downward steadily since the early 1990’s. As Figure 2 illustrates, in 1992 Germany accounted for about 40 percent of Austria’s exports; by 2004, the ratio had fallen to about 31 percent and a downward trend line had emerged. In contrast, Austrian imports from Germany have not trended lower, and instead remained above 40 percent of Austria’s total imports over the same period. Nonetheless, on an overall trade (sum of exports and imports) intensity basis, Austria’s trade with Germany has been trending lower since 1992.

Figure 2.
Figure 2.

Austria’s Trade Intensity with Germany, 1992-2004

(In percent)

Citation: IMF Staff Country Reports 2005, 249; 10.5089/9781451802375.002.A002

Source: IMF, Direction of Trade Statistics.

64. German and Austrian business cycles appear to be less synchronized than they once were. Estimating the degree of comovement among the growth rates of key Austrian aggregates and the German economy suggests that there has been some decline in comovement in recent years. For example, the correlation between Austrian and German output growth rates appears to have peaked by the mid-to-late 1990s, with a correlation coefficient of about 0.9, calculated over a ten-year rolling window (see Figure 3).35 By 2004, the correlation coefficient had fallen to just under 0.8. Moreover, the ten-year rolling correlation between the growth rates of Austria’s real GDP (GDP) and Germany’s domestic demand (DD)36 shows a more pronounced downward shift, since the late 1990s, in the comovement measure. This observation reflects the divergence in recent years between the growth rates of the Austrian economy, which averaged 1.4 percent between 2002 and 2004, and German domestic demand, which contracted in each of those three years.

Figure 3.
Figure 3.

Austria and Germany: Output Comovement,

(Ten-year rolling correlation)

Citation: IMF Staff Country Reports 2005, 249; 10.5089/9781451802375.002.A002

Source: IMF, World Economic Outlook.

C. Austria’s Integration with the CEECs

65. Austria’s economic performance is affected by its growing economic and financial links with the CEECs. Austria’s increasing economic and financial ties with the region have helped diversify its economy in recent years and cushion it from softer conditions in Western Europe. Indeed, among the EU-15 economies, Austria is one of the countries that is likely to have benefited most from the transition in the CEECs. A study by Fidrmuc and others (2002) illustrates the benefits that Austria has enjoyed as a small, open economy situated close to the CEECs and with strong historical ties to the region. For example, between 1989 and 2000, while CEECs’ share of imports from EU-15 countries rose, on average, from about 1 percent to 5 percent, the respective share of CEECs’ imports from Austria increased from 5 percent to 13 percent. Austrian foreign direct investments in the CEECs have also played a critical role in the integration process, especially in the financial sector. The market share of Austrian banks’ in the CEECs, by total assets, has collectively reached approximately 20 percent, while in several CEECs this share is appreciably larger.37 This is a significant accomplishment, considering the Austrian economy accounts for only about 2.5 percent of EU-15 GDP. Furthermore, in 2004, the three largest Austrian banks all derived more than 40 percent of their pretax earnings from operations in the CEECs.38

66. Austrian trade and direct investment links with the CEECs are ahead of most of EU-15 countries. Data for 2004 on EU-15 trade links with the Czech Republic, Hungary, and Poland, show that Austria ranks fourth behind Germany, Italy and France, accounting for 7.5 percent of EU-15 trade (exports plus imports) with those countries. However, when corrected for the size of each country’s GDP, Austria ranks highest in a trade intensity index (see Table 1).

Table 1.

Distribution of EU-15 Trade (Exports plus Imports) with the Czech Republic, Hungary, and Poland (CHP) combined, 2004

article image
Source: IMF, World Economic Oulook; and staff estimates.

Trade with CHP divided by a country’s share of EU-15 GDP, in percent of total.

67. The CEECs today represent a critical destination for Austria’s access to new markets. Figure 4 shows the strong upward movement in Austria’s trade links with the CEECs, which have risen in both U.S. dollar terms and as a share of Austria’s global trade. For example, exports to the CEECs, which represented 7.8 percent of Austria’s total exports in 1992 are nearly twice as high today.

Figure 4.
Figure 4.

Austria’s Trade Intensity with CEECs, 1992-2004

(In percent of total trade)

Citation: IMF Staff Country Reports 2005, 249; 10.5089/9781451802375.002.A002

Source: IMF, Direction of Trade Statistics.

68. On the investment side, Austrian FDI in the CEECs has risen significantly in recent years, reaching close to EUR 16 billion on a cumulative basis between 1995 and 2003 (see Figure 5). On an annual basis, Austrian FDI in the CEECs had risen to about 2 percent of Austrian GDP by 2002.39

Figure 5.
Figure 5.

Austrian FDI in CEECs, 1995-2003

(In billions of euros)

Citation: IMF Staff Country Reports 2005, 249; 10.5089/9781451802375.002.A002

Source: Eurostat.

69. Stronger Austrian ties to the CEECs appear to have coincided with a weaker relationship with Germany. On the trade side, it seems clear that Austria has been successful in diversifying its export markets in the direction of the CEECs, which have become an important source of export earnings for Austrian businesses. In the German context, this is particularly noticeable, as the difference between Germany and the CEECs with regard to their respective market share contributions to Austrian exports shrank from 32 percentage points in 1992 to about 16 percentage points in 2004. Thus, in terms of exports, the increased integration with the CEECs has compensated for the delinking process described above.

uA02fig01

Germany--CEECs Difference in Contribution to Austrian Exports, 1992-2004

(In percent of total Austrian exports)

Citation: IMF Staff Country Reports 2005, 249; 10.5089/9781451802375.002.A002

Source: IMF Direction of T rade Statistics.Note: The sharp dec line in 1997, which subs equently reverted back to the downward trend line, reflects significant growth in that year in Austria’s exports to Poland (50 percent) and Hungary (25 percent), and a marginal decline in exports to Germany (5 percent).

70. Weaker comovement indicators with Germany might be associated with stronger comovement indicators with the CEECs. Recent empirical studies have shown that enhanced bilateral trade integration has been associated with a greater degree of business cycle synchronization. For example, Kose, Prasad, and Terrones (2003) find that greater trade and financial linkages seems to have strengthened the comovement of major macroeconomic aggregates across industrial countries. Kose, Meredith, and Towe (2004) show that, following the launch of the North American Free Trade Agreement (NAFTA), business cycles in Mexico and the United States have become significantly more synchronized, with marked increases in the cross-country correlations of key macroeconomic aggregates.

71. The estimated comovement of output growth for Austria and Hungary—Austria’s largest trading partner among the CEECs—shows a steady increase in the synchronization of their business cycles in recent years. The correlation of the two countries’ real GDP growth rates, computed over a five-year rolling window,40 rose steadily from about 0.2 in the 1992-1996 period to 0.9 in the 1999-2004 period. On the other hand, when similar comovement measures are applied to Poland, Slovenia, and the Czech Republic, no clear trend emerges. That said, in the cases of both Poland and Slovenia, the estimated correlation coefficients have remained around 0.8 in recent years. Possible explanations for the high correlation with Hungary are not only Hungary’s status as Austria’s largest trading partner within the CEECs, accounting for roughly 25 percent of Austria’s exports to the group, but also Hungary’s position as the second-largest recipient of Austrian FDI in the region in the 1995-2003 period. The relatively weak correlation with the economy of the Czech Republic, despite its being Austria’s second-largest trading partner in the region and the largest recipient of Austrian FDI, is likely related to the Czech Republic’s 1997 currency crisis, which led to two consecutive years of real GDP contraction.41

uA02fig02

Austria and Hungary: Output Comovement, 1996-2004

(Five-year rolling correlation of real GDP growth rates)

Citation: IMF Staff Country Reports 2005, 249; 10.5089/9781451802375.002.A002

Source: IMF, World Economic Outlook.
uA02fig03

Austria and the Czech Republic/Poland/Slovenia: Output Comovement

(Five-year rolling correlation of real GDP growth rates)

Citation: IMF Staff Country Reports 2005, 249; 10.5089/9781451802375.002.A002

Source: IMF,World Economic Outlook.

72. With output growth rates in the CEECs expected to exceed that of Western European countries in the years ahead,42 Austria’s increased exposure to the region might facilitate higher growth but also increase volatility. Arora and Vamvakidis (2005) show empirically that economic conditions in trading-partner countries matter for growth; that is, a country’s economic growth is positively influenced by both the growth rate and relative income level of its trading partners. Indeed, faster-growing CEECs have helped drive stronger output growth rates in Austria in recent years, primarily through the expansion in Austrian exports and investments in the region. That said, it is difficult to assess at this time whether this reflects a temporary phenomenon or a more lasting structural shift, given the short time period of the post-transition phase. Similarly, it is too early to judge whether the integration with more volatile economies in Central and Eastern Europe will also lead to greater volatility of the Austrian economy.43 The evolution of Austria’s output volatility, since 1996, appears to exhibit a modest upward trend, but here too, due to limited data availability, cautious is required in assessing the permanent nature of this trend.44

uA02fig04

Austria’s Output Volatility, 1996-2004

(Standard deviation of HP-filtered quarterly year-on-year real GDP growth rates, five-year rolling window)

Citation: IMF Staff Country Reports 2005, 249; 10.5089/9781451802375.002.A002

Source: IMF, World Economic Outlook.

D. Gravity Model Application

73. The objective of employing the gravity model in this paper is to assess, econometrically, whether Austria’s trade patterns with Germany and Hungary are consistent with estimated model predictions, and whether those estimates reflect the process of delinking and integration discussed above.45 The gravity model of international trade has been used extensively in empirical studies analyzing patterns of bilateral trade. Eichengreen and Irwin (1998) noted that what made the application of the gravity model so popular was its ability to explain the variation in bilateral trade flows across a wide variety of countries and time periods, and that “few aggregate economic relationships are as robust.”

74. The typical gravity model specification relates bilateral trade to GDP in each country, their per capita incomes, and the distance between them. An expanded version of this model includes additional variables typically reflecting geographical, cultural, and historical factors that have been found to be statistically relevant for bilateral trade patterns (e.g., area size of country and dummy variables for contiguity, membership in regional trade arrangements, common language, landlocked vs. island economies, history of colonization). On a bilateral basis, trade between countries i and j, in year t, is estimated as a log-linear function and is generally given by

In(Tradeijt)=β0+β1In(GDPiGDPj)+β2In(GDPpciGDPpcj)t+β3In(Distij)+Σz=1nθzX+ϵijt,

where Tradeij is the value of bilateral trade, GDPiGDPj is the product of the two countries’ real GDPs, GDPpci GDPpcj is the product of the two countries’ per capita income, Distij is the straight-line distance between the economic centers of the two countries,

Σz=1nθzX represents the set of geographical/cultural/historical variables, and ∊ is a randomly distributed error term. As trade is expected to rise with GDP and per capita income, and to fall with distance, β1 and β2 ought to reflect a positive estimate, while β3 should reflect a negative estimate.

75. The analysis below draws on two separate panel data sets, Rose (2004) and Bussière, Fidrmuc, and Schantz (BFS, 2005).46 The former is a very large panel, covering 175 countries for the period 1950-99, while the latter comprises 61 countries for the period 1980-2003.47 The BFS data set is used to extend the analysis through the period 1998-2003. The cross-country time-series data in both panels include annual observations for the respective periods. Trade is defined by merchandise trade.

76. Based on the equation depicted above, a gravity model is estimated with an ordinary least squares (OLS) regression.48 The results are presented in Table 2. The first column reflects the results using the Rose data for 1950-97, and the second column presents the results using the BFS data for 1980-2003. The results in the first column are generated from a close variant of the “benchmark” regression in Rose (2004). The regression includes dummies for free trade areas (EU, NAFTA, Mercosur, and ASEAN) and excludes the dummies for membership in the GATT/WTO, which Rose had found to be statistically insignificant. In the regression using the BFS data, some of the geographical and historical dummies that entered the former regression (e.g., history of colonization, landlocked vs. island economies) are missing, but the coefficients on key variables—real GDP, real GDP per capita, distance, and contiguity—are nonetheless statistically the same. Both columns reflect OLS estimations with time-year effects. For a robustness check, the model was also estimated with country-specific fixed effects and random effects, and the results were statistically similar.

Table 2.

Results for the Cross-Section Regression

article image
Notes: OLS with year effects (intercept and year effects not reported).Standard errors are in parentheses.* Indicates significance at the 5 percent level;** at 1 percent. See Appendix for definition of variables.

77. The results are broadly consistent with those found in the literature and track closely the estimated coefficients in Rose (2004) and BFS (2005). Indeed, the results are consistent with the model’s prediction: bilateral trade rises with real GDP and per capita income and falls with distance, and the coefficients on these three variables are statistically significant. Additionally, some of the key geographical and cultural variables also appear to be explained well by the model, such as the positive coefficients on contiguity and common language.

78. Applying the estimated coefficients from Table 2 to the corresponding bilateral trade equations allows us to calculate the predicted levels of trade according to the model. In turn, these levels can be used to examine whether the actual (observed) trade levels are below or above the model-based predictions. If the observed levels are below predicted levels, one could expect trade to grow faster than would be predicted by real GDP growth rates, per-capita income, distance, and so forth. Similarly, if the observed levels are above the predicted levels, one could expect trade to grow more slowly than would be predicted by the gravity model’s explanatory variables. As with any model application, one should be aware of the limitations of the model. In particular, the residual may not fully capture all the intrinsic elements that are specific to Austria’s bilateral trade patterns.

79. The observed and predicted bilateral trade levels for Austria and Germany and Austria and Hungary, as depicted in Figures 6 and 7, support the hypothesis of delinking and integration.49 The specified gravity model suggests that Austria’s bilateral trade with Germany was less than predicted (Figure 6). However, as suggested by the model, the gap between the predicted and observed values had closed (by the late 1980s). The gap has widened again below the predicted levels since the early 1990s, and this trend appears to be consistent with the aforementioned discussion about the gradual delinking process with Germany. Similarly, in the case of Austria and Hungary (Figure 7), the widening of the gap above the predicted levels throughout the 1980s and 1990s seems to reflect the emergence of Austria’s increased integration with Hungary.

E. Conclusion

80. The main conclusion of this study is that Austria’s economic performance in recent years appears to have been driven less by developments in Germany than in the past, while links with the economies of Central and Eastern Europe have become stronger. In particular, Austria’s trade links today are relatively weaker with Germany, notwithstanding that Germany remains Austria’s largest trading partner by far, and stronger with the CEECs. The Austrian and German business cycles, particularly with respect to German domestic demand, are less synchronized than they once were, and Austrian companies are increasingly looking to the CEECs to diversify their investment opportunities. The application of the gravity model underscores the assessment that a measured delinking process with Germany may be underway and seems to support the assertion that this delinking has, in part, been associated with Austria’s enhanced integration with the CEECs, such as Hungary. The enhanced links to the CEECs have the potential to provide an anchor for sustainable Austrian growth in the future, but at the same time the integration with faster-growing economies could portend higher output volatility for Austria as well.

Figure 6a.
Figure 6a.

Austria and Germany: Observed vs. Predicted Trade, 1970-97

(Rose data set, in logs)

Citation: IMF Staff Country Reports 2005, 249; 10.5089/9781451802375.002.A002

Figure 6b.
Figure 6b.

Austria and Germany: Observed vs. Predicted Trade, 1998-2003

(BFS data set, in logs)

Citation: IMF Staff Country Reports 2005, 249; 10.5089/9781451802375.002.A002

Figure 7a.
Figure 7a.

Austria and Hungary: Observed vs. Predicted Trade, 1970-97

(Rose data set, in logs)

Citation: IMF Staff Country Reports 2005, 249; 10.5089/9781451802375.002.A002

Figure 7b.
Figure 7b.

Austria and Hungary: Observed vs. Predicted Trade, 1998-2003

(BFS data set, in logs)

Citation: IMF Staff Country Reports 2005, 249; 10.5089/9781451802375.002.A002

APPENDIX Definitions of Variables Used in the Cross-Section Regression

The variables included in Σz=1nθzX are defined as follows:

Contiguous border–a binary dummy variable that is unity if countries i and j share a common border and zero otherwise.

Common language a binary dummy variable that is unity if i and j share a common language and zero otherwise.

Landlocked–the number of landlocked countries in the country pair (i.e., 0, 1, or 2).

Island–the number of island countries in the country pair (0, 1, or 2).

Area–the area size of the country.

Common colonizer–a binary dummy variable that is unity if i and j were ever colonies after 1945 under the same colonizer.

Ever colony–a binary dummy variable that is unity if i ever colonized j, or vice versa.

Currently colonized–a binary dummy variable that is unity if i were a colony of j at time t, or vice versa.

Common country a binary dummy variable that is unity if i and j were part of the same country during the sample period.

EU, NAFTA, MERCOSUR, ASEAN–binary dummy variables that are unity if i and j both belong to the same regional free trade arrangement.

GSP–a binary dummy variable that is unity if i were a Generalized System of Preferences (GSP) beneficiary of j, or vice versa.

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31

Prepared by Natan Epstein.

32

For the purpose of the analysis in this paper, the group of CEECs is defined as Bulgaria, Croatia, the Czech Republic, Hungary, Poland, Romania, the Slovak Republic, and Slovenia. This group represents the bulk of Austria’s trade and foreign direct investment (FDI) links with the region.

33

In 2004, Austria’s GDP per capita was ranked seventh by Eurostat among OECD countries (per purchasing power standards).

34

By comparison, the next three largest destinations for Austria’s exports are Italy, the United States, and Switzerland, which account for 9, 6, and 5 percent of Austrian exports, respectively.

35

Computed over the previous ten-year window; for example, the 0.89 correlation coefficient in 1999 is estimated over the period 1989-99, where the observation for 1991 is omitted in order to abstract from the large jump in the data series associated with the German unification.

36

Defined as private consumption plus gross fixed capital formation.

37

For example, in the Czech Republic, the Slovak Republic, and Croatia, the market shares have reached about 30 percent, 45 percent, and 40 percent, respectively, according to Austrian National Bank figures.

38

Source: Austrian National Bank.

39

Demekas and others (2005) note that the sources of FDI in Southeastern Europe are highly concentrated among five countries, of which Austria ranks second behind Germany.

40

While comovement with Germany was estimated over a ten-year rolling window, limited data for the transition period render the five-year rolling window more appropriate for the CEECs.

41

The economy of the Czech Republic contracted by 0.8 percent and 1.0 percent in 1997 and 1998, respectively, while Austria’s real GDP expanded by 1.6 percent and 3.9 percent during the same period.

42

IMF, World Economic Outlook, April 2005.

43

In the case of Mexico’s increased linkages to a less volatile U.S. economy, Kose, Meredith, and Towe (2004) note that the greater integration with the United States has brought about a decrease in Mexico’s output volatility.

44

In this figure, volatility is measured as the standard deviation of the Hodrick-Prescott-filtered quarterly—year-on-year—real GDP growth rate series and is computed over a five-year rolling window.

45

We use Hungary as a representative country for the CEECs, since it is Austria’s key trading-partner in the region and long-run data for most of the other countries in the region are more limited.

46

In Rose (2004), the gravity model is used to analyze the effects of multilateral trade agreements on trade, while in BFS (2005) the gravity model is adopted to analyze trade linkages between the euro area and Central and Southeastern Europe.

47

For data consistency reasons, we used the Rose panel data through 1997.

48

For definitions of the variables included in Σz=1nθzX see the Appendix.

49

The figures are separated so as to distinguish between the two panel data sets (Figures 6a and 7a reflect the Rose data, while Figures 6b and 7b reflect the BFS data). While the two regressions produce similar estimated coefficients, the panels are not identical and thus the two series are not continuous.

Austria: Selected Issues
Author: International Monetary Fund
  • View in gallery

    Austria, Germany, and Euro Zone: Real GDP Growth, 1994-2004

    (In percent)

  • View in gallery

    Austria’s Trade Intensity with Germany, 1992-2004

    (In percent)

  • View in gallery

    Austria and Germany: Output Comovement,

    (Ten-year rolling correlation)

  • View in gallery

    Austria’s Trade Intensity with CEECs, 1992-2004

    (In percent of total trade)

  • View in gallery

    Austrian FDI in CEECs, 1995-2003

    (In billions of euros)

  • View in gallery

    Germany--CEECs Difference in Contribution to Austrian Exports, 1992-2004

    (In percent of total Austrian exports)

  • View in gallery

    Austria and Hungary: Output Comovement, 1996-2004

    (Five-year rolling correlation of real GDP growth rates)

  • View in gallery

    Austria and the Czech Republic/Poland/Slovenia: Output Comovement

    (Five-year rolling correlation of real GDP growth rates)

  • View in gallery

    Austria’s Output Volatility, 1996-2004

    (Standard deviation of HP-filtered quarterly year-on-year real GDP growth rates, five-year rolling window)

  • View in gallery

    Austria and Germany: Observed vs. Predicted Trade, 1970-97

    (Rose data set, in logs)

  • View in gallery

    Austria and Germany: Observed vs. Predicted Trade, 1998-2003

    (BFS data set, in logs)

  • View in gallery

    Austria and Hungary: Observed vs. Predicted Trade, 1970-97

    (Rose data set, in logs)

  • View in gallery

    Austria and Hungary: Observed vs. Predicted Trade, 1998-2003

    (BFS data set, in logs)