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Article

Alfred Duncan and Charles Nolan

In recent decades, macroeconomic researchers have looked to incorporate financial intermediaries explicitly into business-cycle models. These modeling developments have helped us to understand the role of the financial sector in the transmission of policy and external shocks into macroeconomic dynamics. They also have helped us to understand better the consequences of financial instability for the macroeconomy. Large gaps remain in our knowledge of the interactions between the financial sector and macroeconomic outcomes. Specifically, the effects of financial stability and macroprudential policies are not well understood.

Article

Most applied researchers in macroeconomics who work with official macroeconomic statistics (such as those found in the National Accounts, the Balance of Payments, national government budgets, labor force statistics, etc.) treat data as immutable rather than subject to measurement error and revision. Some of this error may be caused by disagreement or confusion about what should be measured. Some may be due to the practical challenges of producing timely, accurate, and precise estimates. The economic importance of measurement error may be accentuated by simple arithmetic transformations of the data, or by more complex but still common transformations to remove seasonal or other fluctuations. As a result, measurement error is seemingly omnipresent in macroeconomics. Even the most widely used measures such as Gross Domestic Products (GDP) are acknowledged to be poor measures of aggregate welfare as they omit leisure and non-market production activity and fail to consider intertemporal issues related to the sustainability of economic activity. But even modest attempts to improve GDP estimates can generate considerable controversy in practice. Common statistical approaches to allow for measurement errors, including most factor models, rely on assumptions that are at odds with common economic assumptions which imply that measurement errors in published aggregate series should behave much like forecast errors. Fortunately, recent research has shown how multiple data releases may be combined in a flexible way to give improved estimates of the underlying quantities. Increasingly, the challenge for macroeconomists is to recognize the impact that measurement error may have on their analysis and to condition their policy advice on a realistic assessment of the quality of their available information.

Article

Lasse Aaskoven and David Dreyer Lassen

The political budget cycle—how elections affect government fiscal policy—is one of the most studied subjects in political economy and political science. The key theoretical question is whether incumbent governments can time or structure public finances in ways that improve their chances of reelection; the key empirical question is whether this in fact happens. The incentives of incumbents to engage in such electioneering are governed by political institutions, observability of political choices, and their consequences, as well as voter knowledge, and both theoretical and empirical studies on political budget cycles have recently focused on conditions under which such cycles are likely to obtain. Much recent research focuses on subnational settings, allowing comparisons of governments in similar institutional environments, and a consensus on the presences of cycles in public finances—and in the reporting of public finances—is beginning to emerge.

Article

How do economic conditions affect public opinion about climate change? Since the early days of the modern environmental movement, people have debated three main perspectives on how economic conditions impact environmental attitudes. The post-materialism perspective suggests that social and individual affluence leads to increasing concern and demands for action on climate change through long-run cultural change. A second view suggests that attitudes about climate change are shaped largely independently of economic conditions and reflect the emergence of a new environmental paradigm. A third view, associated with ecological modernization theory, suggests that attitudes about climate change are shaped in important ways by short-term economic factors, such as economic self-interest, and are likely to vary among citizens over time. While all of these perspectives have merit, we emphasize the impact of macroeconomic risk and business cycle fluctuations in shaping public attitudes toward climate change and more general aspects of environmental policy. Rising unemployment rates, for example, tend to be associated with declines in concern about environmental problems. This is a trend that is repeated across more than four decades and multiple recessions and recoveries dating back to the 1970s. Although it is obviously a more recently recognized environmental problem, public attitudes about climate change are also affected considerably by short-run economic conditions. This fact can influence the possibilities for policy reform. Through a process of motivated reasoning, in which immediate concerns and preferences to address economic risk lead individuals to adjust other attitudes about the environment, public concerns about climate change have ebbed and flowed with the business cycle. Other economic factors—such as societal affluence, personal employment status, or income—have more limited effects on attitudes about climate change, at least in most developed countries. The impact of economic risk on public attitudes about climate change has important implications for policy reform in democratic societies, because public support matters. While partisanship and ideology are frequently cited as explanations for fluctuating public opinion about climate change, macroeconomic risk offers a complementary explanation, which suggests that the framing and timing of environmental policy initiatives is as important as ideological acceptability. Positioning environmental actions or initiatives in better economic conditions, emphasizing immediate economic benefits, and countering unwarranted beliefs about personal costs, especially during challenging economic circumstances, should improve the prospects for efforts to address climate change.

Article

Cristina Bellés-Obrero and Judit Vall Castelló

The impact of macroeconomic fluctuations on health and mortality rates has been a highly studied topic in the field of economics. Many studies, using fixed-effects models, find that mortality is procyclical in many countries, such as the United States, Germany, Spain, France, Pacific-Asian nations, Mexico, and Canada. On the other hand, a small number of studies find that mortality decreases during economic expansion. Differences in the social insurance systems and labor market institutions across countries may explain some of the disparities found in the literature. Studies examining the effects of more recent recessions are less conclusive, finding mortality to be less procyclical, or even countercyclical. This new finding could be explained by changes over time in the mechanisms behind the association between business cycle conditions and mortality. A related strand of the literature has focused on understanding the effect of economic fluctuations on infant health at birth and/or child mortality. While infant mortality is found to be procyclical in countries like the United States and Spain, the opposite is found in developing countries. Even though the association between business cycle conditions and mortality has been extensively documented, a much stronger effort is needed to understand the mechanisms behind the relationship between business cycle conditions and health. Many studies have examined the association between macroeconomic fluctuations and smoking, drinking, weight disorders, eating habits, and physical activity, although results are rather mixed. The only well-established finding is that mental health deteriorates during economic slowdowns. An important challenge is the fact that the comparison of the main results across studies proves to be complicated due to the variety of empirical methods and time spans used. Furthermore, estimates have been found to be sensitive to the use of different levels of geographic aggregation, model specifications, and proxies of macroeconomic fluctuations.

Article

Structural vector autoregressions (SVARs) represent a prominent class of time series models used for macroeconomic analysis. The model consists of a set of multivariate linear autoregressive equations characterizing the joint dynamics of economic variables. The residuals of these equations are combinations of the underlying structural economic shocks, assumed to be orthogonal to each other. Using a minimal set of restrictions, these relations can be estimated—the so-called shock identification—and the variables can be expressed as linear functions of current and past structural shocks. The coefficients of these equations, called impulse response functions, represent the dynamic response of model variables to shocks. Several ways of identifying structural shocks have been proposed in the literature: short-run restrictions, long-run restrictions, and sign restrictions, to mention a few. SVAR models have been extensively employed to study the transmission mechanisms of macroeconomic shocks and test economic theories. Special attention has been paid to monetary and fiscal policy shocks as well as other nonpolicy shocks like technology and financial shocks. In recent years, many advances have been made both in terms of theory and empirical strategies. Several works have contributed to extend the standard model in order to incorporate new features like large information sets, nonlinearities, and time-varying coefficients. New strategies to identify structural shocks have been designed, and new methods to do inference have been introduced.

Article

Charles Ka Yui Leung and Cho Yiu Joe Ng

This article summarizes research on the macroeconomic aspects of the housing market. In terms of the macroeconomic stylized facts, this article demonstrates that with respect to business cycle frequency, there was a general decrease in the association between macroeconomic variables (MV), such as the real GDP and inflation rate, and housing market variables (HMV), such as the housing price and the vacancy rate, following the global financial crisis (GFC). However, there are macro-finance variables, such as different interest rate spreads, that exhibited a strong association with the HMV following the GFC. For the medium-term business cycle frequency, some but not all patterns prevail. These “new stylized facts” suggest that a reconsideration and refinement of existing “macro-housing” theories would be appropriate. This article also provides a review of the corresponding academic literature, which may enhance our understanding of the evolving macro-housing–finance linkage.

Article

Candace Archer

Numerous crises have occurred since the beginnings of the modern economic system, from the Dutch Tulip Mania of 1636 and the South Sea Bubble of 1720 to the Dollar Crisis and Asian Financial Crisis. Scholars have written about the causes and remedies of financial crisis, resulting in a substantial amount of literature on the subject especially after the Great Depression. The writing on financial crisis declined between the end of World War II and the monetary crises in the early 1970s, but has become vibrant again since the 1980s. Some of the earliest voices that contributed to the intellectual history of studying financial crisis include Adam Smith, Karl Marx, David Ricardo, Walter Bagehot, and John Maynard Keynes. These men provided the foundation for understanding the central issues and questions about financial crisis and influenced the debates and scholarship that followed. One such debate involved monetarists vs. business cycle theorists. The monetarists argue that crises are caused by changes in the money supply, while those favoring a business cycle approach insist that expansions and contractions are part of economic interactions and so the economy will at times experience crises. As crises continue to affect both domestic and global financial markets, more perspectives are added to the discussion, including those that invoke rational expectations and economic models, along with those that draw from international political economy. There are also questions that remain unanswered, such as the issue of crisis response and that of financial fragility.

Article

Alessandro Rebucci and Chang Ma

This paper reviews selected post–Global Financial Crisis theoretical and empirical contributions on capital controls and identifies three theoretical motives for the use of capital controls: pecuniary externalities in models of financial crises, aggregate demand externalities in New Keynesian models of the business cycle, and terms of trade manipulation in open-economy models with pricing power. Pecuniary and demand externalities offer the most compelling case for the adoption of capital controls, but macroprudential policy can also address the same distortions. So capital controls generally are not the only instrument that can do the job. If evaluated through the lenses of the new theories, the empirical evidence reviewed suggests that capital controls can have the intended effects, even though the extant literature is inconclusive as to whether the effects documented amount to a net gain or loss in welfare terms. Terms of trade manipulation also provides a clear-cut theoretical case for the use of capital controls, but this motive is less compelling because of the spillover and coordination issues inherent in the use of control on capital flows for this purpose. Perhaps not surprisingly, only a handful of countries have used capital controls in a countercyclical manner, while many adopted macroprudential policies. This suggests that capital control policy might entail additional costs other than increased financing costs, such as signaling the bad quality of future policies, leakages, and spillovers.

Article

Yong Song and Tomasz Woźniak

Markov switching models are a family of models that introduces time variation in the parameters in the form of their state, or regime-specific values. This time variation is governed by a latent discrete-valued stochastic process with limited memory. More specifically, the current value of the state indicator is determined by the value of the state indicator from the previous period only implying the Markov property. A transition matrix characterizes the properties of the Markov process by determining with what probability each of the states can be visited next period conditionally on the state in the current period. This setup decides on the two main advantages of the Markov switching models: the estimation of the probability of state occurrences in each of the sample periods by using filtering and smoothing methods and the estimation of the state-specific parameters. These two features open the possibility for interpretations of the parameters associated with specific regimes combined with the corresponding regime probabilities. The most commonly applied models from this family are those that presume a finite number of regimes and the exogeneity of the Markov process, which is defined as its independence from the model’s unpredictable innovations. In many such applications, the desired properties of the Markov switching model have been obtained either by imposing appropriate restrictions on transition probabilities or by introducing the time dependence of these probabilities determined by explanatory variables or functions of the state indicator. One of the extensions of this basic specification includes infinite hidden Markov models that provide great flexibility and excellent forecasting performance by allowing the number of states to go to infinity. Another extension, the endogenous Markov switching model, explicitly relates the state indicator to the model’s innovations, making it more interpretable and offering promising avenues for development.

Article

George W. Evans and Bruce McGough

Adaptive learning is a boundedly rational alternative to rational expectations that is increasingly used in macroeconomics, monetary economics, and financial economics. The agent-level approach can be used to provide microfoundations for adaptive learning in macroeconomics. Two central issues of bounded rationality are simultaneously addressed at the agent level: replacing fully rational expectations of key variables with econometric forecasts and boundedly optimal decisions-making based on those forecasts. The real business cycle (RBC) model provides a useful laboratory for exhibiting alternative implementations of the agent-level approach. Specific implementations include shadow-price learning (and its anticipated-utility counterpart, iterated shadow-price learning), Euler-equation learning, and long-horizon learning. For each implementation the path of the economy is obtained by aggregating the boundedly rational agent-level decisions. A linearized RBC can be used to illustrate the effects of fiscal policy. For example, simulations can be used to illustrate the impact of a permanent increase in government spending and highlight the similarities and differences among the various implements of agent-level learning. These results also can be used to expose the differences among agent-level learning, reduced-form learning, and rational expectations. The different implementations of agent-level adaptive learning have differing advantages. A major advantage of shadow-price learning is its ease of implementation within the nonlinear RBC model. Compared to reduced-form learning, which is widely use because of its ease of application, agent-level learning both provides microfoundations, which ensure robustness to the Lucas critique, and provides the natural framework for applications of adaptive learning in heterogeneous-agent models.