Globalization, the Business Cycle, and Macroeconomic Monitoring
2011; University of Chicago Press; Volume: 7; Issue: 1 Linguagem: Inglês
10.1086/658307
ISSN2150-8372
AutoresS. Borağan Aruoba, Francis X. Diebold, M. Ayhan Köse, Marco E. Terrones,
Tópico(s)Economic Theory and Policy
ResumoPrevious articleNext article FreeGlobalization, the Business Cycle, and Macroeconomic MonitoringS. Borağan Aruoba, Francis X. Diebold, M. Ayhan Kose, and Marco E. TerronesS. Borağan AruobaUniversity of Maryland Search for more articles by this author , Francis X. DieboldUniversity of Pennsylvania and NBER Search for more articles by this author , M. Ayhan KoseInternational Monetary Fund Search for more articles by this author , and Marco E. TerronesInternational Monetary Fund Search for more articles by this author PDFPDF PLUSFull Text Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinked InRedditEmailQR Code SectionsMoreI. IntroductionIn the modern environment of radically enhanced global macroeconomic and financial linkages, isolated country analysis seems highly insufficient for informed assessment of the state of real activity and, therefore, for informed decision making. Hence we propose and implement a framework for characterizing and monitoring the global business cycle. Our framework is informed by economic theory and structured so as to help inform subsequent economic theory. We apply it to the Group of 7 (G7) countries, and in so doing we extend the empirical research program on the global business cycle along several dimensions.First, we consider the roles played by a large set of macroeconomic indicators when we construct our country and global cycles. The country and global factors that we estimate provide a better characterization of business cycles as they encompass a wide array of activity measures, in the tradition of Burns and Mitchell (1946) and much subsequent research. This contrasts with most of the literature on global business cycles, which uses only quarterly national income and product account data.Second, our comparatively comprehensive set of indicators enables us to provide a systematic characterization of global and national business cycles. In particular, we analyze various statistical properties of cycles, and we relate certain cyclical episodes to the movements in country and global macroeconomic factors. We also study the interaction of activity across countries and with the global cycle.Third, and related, we use our rich set of indicators to explore the evolution of the global business cycle. We emphasize, among other things, whether and how cross-country business cycle synchronization has evolved in response to the forces of globalization. Against this background, we devote special attention to the recent recession.We proceed as follows. In Section II, we review several literatures that bear on our concerns. We first provide a summary of various empirical approaches used to model the global business cycle. Then, considering that our measures of global and national business cycles should help us analyze the evolution of business cycle synchronization, we also review the literature on linkages between globalization and synchronization. The main message is that, although various approaches have been employed, it has been a challenge to construct practical and satisfactory tools for monitoring global business cycles.In Section III, we construct and examine a new G7 data set, which contains a variety of real activity indicators. In particular, we use six widely followed real activity indicators for each country whenever available: employment, GDP, disposable income, industrial production, retail sales, and initial claims for unemployment insurance. Because the indicators are available at different frequencies and dates, they provide valuable and complementary high-frequency information about the state of the economy.In Section IV, we introduce and fit a simple dynamic factor model for real activity separately for each country. We work in a state space framework with multiple indicators and a single latent activity factor, which we extract optimally using the Kalman filter. One distinguishing feature of our approach is that we are able to utilize mixed-frequency data, specifying the model at high frequency and allowing for a potentially large amount of missing data (for the less frequently observed variables). The country factors that we extract explain most of the common variation in underlying country activity indicators, and they are consistent with a number of well-known business cycle episodes in each country. Moreover, we find that the degree of country-factor synchronization has changed over time in response to growing global linkages, which change the importance of common versus country-specific shocks.In Section V, we estimate a hierarchical multicountry model. After obtaining the estimated country factors, we decompose their movements into those coming from a common G7 factor and those coming from idiosyncratic components. The G7 factor measures the global business cycle, capturing common fluctuations in country factors, which are themselves reflections of common movements in underlying activity variables in each country. The G7 factor captures a significant amount of common variation across countries and reflects the major cyclical events of the past 40 years. Moreover, it appears to play different roles at different times in shaping national economic activity. We present conclusions in Section VI.II. An Interpretive Literature ReviewHere we present a brief and selective survey of empirical strategies used to model the global business cycle as relevant for our subsequent development, examining how those strategies have evolved in academic and policy circles over the years. We pay special attention to the links, both theoretical and empirical, between globalization and business cycle synchronization.A. Empirical Modeling of the Global Business CycleAs global linkages have become stronger, the interest in understanding the dynamics of global activity has increased in both academic and policy circles. Many studies use simple measures of global activity, which are often based on a country size weighted average of the major advanced countries’ output growth (see Ahmed et al. 1993). More recently, developments of new econometric methods and advances in computing technology have facilitated the use of more sophisticated approaches, such as dynamic factor models.1 These models have been quite successful in capturing common fluctuations in multiple time series of a large cross section of countries. Some of these models rely on a single measure of aggregate activity, such as output, whereas others employ multiple indicators, including output, consumption, and investment, in order to provide more reliable estimates of global business cycles (see Gregory, Head, and Reynauld 1997; Kose, Otrok, and Whiteman 2003, 2008; Kose, Prasad, and Terrones 2003).2 As we present later in this section, these models have been widely used to study the evolution of global business cycles.In policy circles as well, there has been an increasing appreciation of the importance of well-designed tools to track global economic activity. Approaches employed by policy institutions differ considerably in technical sophistication and scope. The International Monetary Fund (IMF), for example, uses a simple country size weighted average of each member country’s output growth rate to arrive at its estimate of the world output growth.3 Given that the IMF membership includes a rather diverse set of 187 countries, the measure it employs provides a simple and intuitive characterization of global economic activity. However, the measure also has some drawbacks. First, GDP is often available only at a quarterly frequency, making it difficult to monitor global activity at higher frequencies. Second, as much as it is a simple and intuitive measure, it is based on a single indicator, GDP, which is a rather crude measure of activity with a variety of well-known shortcomings.In addition to the simple measures mentioned above, applications of various composite, leading, and coincident indicators have been employed to assess the state of activity in a (functional/regional) group of countries. Well-known examples of these include the Organisation for Economic Cooperation and Development’s (OECD’s) composite leading indicators and the Centre for Economic Policy Research’s (CEPR’s) EuroCOIN. Both indicators are available in a monthly frequency and employ a large number of activity variables. The OECD’s composite leading indicators use various weighting and filtering methods to aggregate information from the underlying activity variables.4 The indicators are intended to provide early signals of turning points in business cycles of various groups of countries, including the OECD area, euro area, Major Five Asia, and G7. However, like most other composite indexes of activity, the OECD’s indexes also lack a well-defined econometric methodology and involve a rather subjective determination of the underlying economic variables and their aggregation.The CEPR’s EuroCOIN is a coincident indicator designed to monitor euro area activity in real time. The index is estimated using a generalized dynamic factor model (see Altissimo et al. 2001). Using a sizable number of data series, including measures of real and financial sector activity and surveys of business and consumer sentiment, the indicator provides an estimate of the monthly growth of euro area GDP.As the discussion so far has shown, although various approaches have been employed, it has been a challenge to construct practical and satisfactory tools to monitor global business cycles. The methodology we use in this paper has several advantages over existing approaches. First, our framework is useful for monitoring global economic activity in real time. Second, our measure of global business cycles captures common movements in a wide range of indicators, such as GDP, income, retail sales, initial claims, employment, and industrial production. We combine the information content of activity measures available at different frequencies (monthly as well as quarterly) to arrive at a monthly measure. Third, our measure of global activity is obtained using linear and exact procedures that are easily reproducible. Fourth, our methodology leads to a coherent analysis of interactions between the global business cycle and country-specific cycles as it employs a well-defined hierarchical structure to estimate these cycles.B. Globalization and Business Cycle SynchronizationA large literature examines the implications of globalization, which is often associated with increased international trade and financial linkages, for the synchronization of international business cycles.1. TheoryEconomic theory has ambiguous predictions about the impact of increased trade and financial linkages on the comovement among macroeconomic aggregates across countries. Stronger trade linkages can lead to a higher or lower degree of comovement depending on the nature of integration and the form of specialization patterns. International trade linkages generate both demand- and supply-side spillovers across countries, which can increase the degree of business cycle synchronization. For example, on the demand side, an investment or consumption boom in one country can generate increased demand for imports, boosting economies abroad. On the supply side, a positive tradable output shock leads to lower prices; hence, imported inputs for other countries become cheaper. Through these types of spillover effects, stronger international trade linkages can result in more highly correlated business cycles across countries.However, both classical and “new” trade theories imply that increased openness (trade linkages) to trade leads to increased specialization. How does increased specialization affect the degree of synchronization? The answer depends on the nature of specialization (intra- vs. interindustry) and the types of shocks (common vs. country-specific). If stronger trade linkages are associated with increased interindustry specialization across countries, then the impact of increased trade depends on the nature of shocks: If industry-specific shocks are more important in driving business cycles, then international business cycle comovement is expected to decrease. If common shocks, which might be associated with changes in demand and/or supply conditions, are more dominant than industry-specific shocks, then this would lead to a higher degree of business cycle comovement.What about the impact of financial integration on the extent of business cycle comovement? Analytically, the effects of financial integration also depend on the nature of shocks and the form of specialization patterns. For example, financial linkages could result in a higher degree of business cycle synchronization by generating large demand-side effects as the changes in equity prices affect the dynamics of wealth. If consumers from different countries have a significant fraction of their investments in a particular stock market, then a decline in that stock market could induce a simultaneous decline in the demand for consumption and investment goods in these countries because of its impact on domestic wealth. Furthermore, contagion effects that are transmitted through financial linkages could also result in heightened cross-country spillovers of macroeconomic fluctuations.However, international financial linkages could decrease the cross-country output correlations as they stimulate specialization of production through the reallocation of capital in a manner consistent with countries’ comparative advantage in the production of different goods. For example, Kalemli-Ozcan, Sorensen, and Yosha (2003) find that there is a significant positive correlation between the degree of financial integration (risk sharing) and specialization of production. In other words, through increasing financial linkages, countries can have a more diversified portfolio and are able to insure themselves against idiosyncratic shocks. This would lead to less correlated cross-country fluctuations in output since it could result in more exposure to industry- or country-specific shocks. However, since such specialization of production would typically be expected to be accompanied by the use of international financial markets to diversify consumption risk, it should result in stronger comovement of consumption across countries.Increased integration could also affect the dynamics of comovement by changing the nature and frequency of shocks. First, as trade and financial linkages get stronger, the need for a higher degree of policy coordination might increase, which, in turn, would raise the correlations between shocks associated with nation-specific fiscal and/or monetary policies. This would naturally have a positive impact on the degree of business cycle synchronization. However, it is not clear, at least in theory, whether increasing trade and financial linkages indeed lead to a growing need for the implementation of coordinated policies. Traditional arguments, based on trade multiplier models, would suggest that increased linkages imply a growing need for international policy coordination (see Oudiz and Sachs 1984). However, recent research by Obstfeld and Rogoff (2002) provides results quite different from those in the previous literature. They argue that integration may in fact diminish the need for policy coordination since international capital markets generate an expanded set of opportunities for cross-country risk sharing.Second, shocks pertaining to changes in productivity could become more correlated if increased trade and financial integration lead to an acceleration in knowledge and productivity spillovers across countries (see Coe and Helpman 1995). More financially integrated economies are able to attract relatively large foreign direct investment flows, which have the potential to generate productivity spillovers.Third, increased financial integration and developments in communication technologies lead to faster dissemination of news shocks in financial markets. This could have a positive impact on the degree of business cycle synchronization if, for example, good news about the future of the domestic economy would increase domestic consumption through its impact on wealth and if consumers in other countries, who hold stocks in the domestic country, raise demand for goods in their countries. In other words, shocks associated with news, which are rapidly transmitted in global financial markets, could lead to a higher degree of interdependence across economic activity in different countries.2. EmpiricsEmpirical studies are also unable to provide a concrete explanation for the impact of stronger trade and financial linkages on the nature of business cycles. There has been a growing research program examining the empirical relationship between increased linkages and the dynamics of business cycle comovement using a variety of methods. A widely popular approach in this literature involves the study of the changes in some simple measures of business cycle comovement over time. Another strand of the literature directly examines how increasing trade and financial linkages affect the business cycle correlations employing various regression models. A third approach uses recently developed econometric methods, such as dynamic factor models, to examine the characteristics of common factors in business cycles.The studies in the first group focus on the evolution of comovement properties of the main macroeconomic aggregates over time in response to changes in the volume of trade and financial flows. The results of these studies indicate that differences in country coverage, sample periods, aggregation methods used to create country groups, and econometric methods employed could lead to diverse conclusions about the temporal evolution of business cycle synchronization. For example, some of these studies find evidence of declining output correlations among industrial economies over the last three decades. Helbling and Bayoumi (2003) find that correlation coefficients between the United States and other G7 countries for the period 1973–2001 are substantially lower than those for 1973–89. In a related paper, Heathcote and Perri (2004) document that the correlations of output, consumption, and investment between the United States and an aggregate of Europe, Canada, and Japan are lower in the period 1986–2000 than in 1972–85. Results by Doyle and Faust (2005) indicate that there is no significant change in the correlations between the growth rate of output in the United States and that in other G7 countries over time.The empirical studies in the second group employ cross-country or cross-region panel regressions to assess the role of global linkages on the comovement properties of business cycles in developed and developing countries. While Imbs (2004, 2006) finds that the extent of financial linkages, sectoral similarity, and the volume of intraindustry trade all have a positive impact on business cycle correlations, Otto, Voss, and Willard (2001) and Baxter and Kouparitsas (2005) document that international trade is the most important transmission channel of business cycles. The results by Kose, Prasad, and Terrones (2003) suggest that both trade and financial linkages have a positive impact on cross-country output and consumption correlations. Calderon, Chong, and Stein (2007) report that international trade linkages lead to higher cross-country business cycle correlations among developed countries than among developing countries.Some other studies employ factor models to study the changes in the degree of business cycle comovement, but those studies also report conflicting findings. Stock and Watson (2005) employ a factor-structural vector autoregression model to analyze the importance of international factors in explaining business cycles in the G7 countries since 1960. They conclude that comovement has fallen in the 1984–2002 period relative to 1960–83 because of the diminished importance of common shocks. Kose, Otrok, and Whiteman (2008) employ a Bayesian dynamic factor model to analyze the evolution of comovement since 1960.5 Using the data of the G7 countries, they document that the common (G7) factor on average explains a larger fraction of output, consumption, and investment volatility in the globalization period (1986–2005) than in the 1960–72 period. They interpret this result as an indication of increasing degree of business cycle synchronization in the age of globalization. Kose, Otrok, and Prasad (2008) employ a dynamic factor model to analyze the evolution of synchronization in a large sample of industrial, emerging, and developing countries. They report that since the mid-1980s there has been a higher degree of synchronization of business cycle fluctuations among the group of industrial economies and among the group of emerging market economies.III. A G7 Real Activity Indicator Data SetWe work with a G7 data set. Although the G7 is a smaller group of countries than we ultimately hope to incorporate, it is nevertheless highly significant and certainly much more encompassing than the United States alone. Indeed the United States is responsible for only 25% of world real output at market exchange rates, whereas the G7 is responsible for 50%.6Partly reflecting our desire to maximize transparency and convenience and partly reflecting the paucity of useful and comparable high-frequency real activity indicator data available for a large group of countries, we adopt a monthly base frequency. This eliminates many of the complications in Aruoba, Diebold, and Scotti (2009), including time-varying system matrices, high-dimensional state vectors, and so forth.For each country we use data matching six economic concepts where available: employment, GDP, disposable income, industrial production, retail sales, and initial claims for unemployment insurance. There are several reasons why we focus on those variables. First, they constitute an integral part of activity indexes that are used to study the direction of the economy by policy institutions, think tanks, and financial markets. While GDP is the most widely followed indicator of aggregate activity, the others move closely with the different phases of the cycle. Initial claims is a leading indicator of business cycle; industrial production, retails sales, and income are coincident indicators; and employment is a lagging indicator. Second, the business cycle dating committees, including the NBER Business Cycle Dating Committee and CEPR Business Cycle Dating Committee, base their decisions on those or closely related indicators. Third, and related, those variables are the ones used to produce the ADS Business Conditions Index based on Aruoba et al. (2009) now provided by the Federal Reserve Bank of Philadelphia, as well as the Conference Board’s composite coincident index, among others. We use the same set of variables here, to the extent possible, for the other countries.We gathered all data in April 2010; the resulting sample ranges from 1970 through 2009. Across the seven countries, we have a total of 37 series observed over (at most) 40 years. The specific statistical series available sometimes differ across countries, although the economic concepts measured are highly similar. For example, we use U.S. payroll employment and Canadian civilian employment. Sources vary, but we rely heavily on the Haver and OECD databases. We measure all indicators in real terms. We use indicators seasonally adjusted by the relevant reporting agency.7 We transform all indicators to logarithmic changes, except initial claims; hence all are flows.We summarize certain aspects of the data in Table 1, which gives for each country the series used, the data source
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