Vector autoregressions with stochastic volatility (SV) are widely used in macroeconomic forecasting and structural inference. The SV component of the model conveniently allows for time variation in the variance-covariance matrix of the model’s forecast errors. In turn, that feature of the model generates time variation in predictive densities. The models are most commonly estimated with Bayesian methods, most typically Markov chain Monte Carlo methods, such as Gibbs sampling. Equation-by-equation methods developed since 2018 enable the estimation of models with large variable sets at much lower computational cost than the standard approach of estimating the model as a system of equations. The Bayesian framework also facilitates the accommodation of mixed frequency data, non-Gaussian error distributions, and nonparametric specifications. With advances made in the 21st century, researchers are also addressing some of the framework’s outstanding challenges, particularly the dependence of estimates on the ordering of variables in the model and reliable estimation of the marginal likelihood, which is the fundamental measure of model fit in Bayesian methods.
Article
Eric Monnet
The historical evolution of the role of central banks has been shaped by two major characteristics of these institutions: they are banks and they are linked—in various legal, administrative, and political ways—to the state. The history of central banking is thus an analysis of how central banks have ensured or failed to ensure the stability of the value of money and the credit system while maintaining supportive or conflicting relationships with governments and private banks. Opening the black box of central banks is necessary to understanding the political economy issues that emerge from the implementation of monetary and credit policy and why, in addition to macroeconomic effects, these policies have major consequences on the structure of financial systems and the financing of public debt. It is also important to read the history of the evolution of central banks since the end of the 19th century as a game of countries wanting to adopt a dominant institutional model. Each historical period was characterized by a dominant model that other countries imitated - or pretended to imitate while retaining substantial national characteristics - with a view to greater international political and financial integration. Recent academic research has explored several issues that underline the importance of central banks to the development of the state, the financial system and on macroeconomic fluctuations: (a) the origin of central banks; (b) their role as a lender of last resort and banking supervisor; (c) the justifications and consequences of domestic macroeconomic policy objectives - inflation, output, etc. -of central banks (monetary policy); (d) the special loans of central banks and their role in the allocation of credit (credit policy); (e) the legal and political links between the central bank and the government (independence); (f) the role of central banks concerning exchange rates and the international monetary system; (g) production of economic research and statistics.
Article
Charles Ka Yui Leung
The earlier literature on macroeconomics focused on determining aggregate variables such as gross domestic product (GDP), inflation rate, and unemployment rate. It had little interaction with the literature on housing. The importance of housing in the macroeconomy has been recently discovered, and the macro-housing field is in development. The recent literature addresses several policy-relevant issues that are important for macroeconomics and housing strands of literature.
One of the significant developments is the research on the rental market, as a considerable portion of the world population are renters. For instance, the impact of some macroeconomic policies depends on how easily a unit is converted between rental or owner-occupied housing. Just as failure to keep up with the mortgage payment in owner-occupied housing would lead to bankruptcy, failure to pay rent as the contract described could lead to eviction. The literature has started to investigate the causes and costs of such displacement. Some authors also explore whether public rental housing is a desirable policy.
Another active research area is affordability. Some people could afford to rent but not own housing in some cities. Some may move to places where they can be house owners. The literature has started to explore such interactions of the locational choice with the tenure choice (i.e., to rent or to own).
The durability of housing makes it a long-term investment. Hence, the timing and pricing of the current period housing transaction depend on the expectations of future prices. Moreover, the recent period transactions in the housing market could, in turn, affect future prices. Therefore, self-fulfilling prophecies are possible, and it is crucial to study the formation and evolution of expectations in the housing market. Some researchers have taken up the challenges and made some progress.
Last but not least, the literature has extended from a single-market to a multi-market setting. Emerging literature studies the local housing and labor market, such as the county level, and brings results that challenge conventional wisdom. In response, a few authors have developed sophisticated multi-regional dynamic general equilibrium models to match the cross-sectional and time series facts and maintain the forward-looking assumption in the macroeconomics tradition. Those new models also help us to identify shocks that are not directly observable to econometricians and, at the same time, are essential to account for cross-sectional economic facts. They can bring us closer to the actual situation.
In sum, the recent developments in macro-housing literature are exciting and encouraging. They would accompany scholars on the journey of evidence-based public policy formation.
Article
Xiangtao Meng, Katheryn N. Russ, and Sanjay R. Singh
For hundreds of years, policymakers and academics have puzzled over how to add up the effects of trade and trade barriers on economic activity. The literature is vast. Trade theory generally focuses on the question of whether trade or trade barriers, like tariffs, make people and firms better off using models of the real economy operating at full employment and a net-zero trade balance. They yield powerful fundamental intuition but are not well equipped to address issues such as capital accumulation, the role of exchange rate depreciation, monetary policy, intertemporal optimization by consumers, or current account deficits, which permeate policy debates over tariffs. The literature on open-economy macroeconomics provides additional tools to address some of these issues, but neither literature has yet been able to answer definitively the question of what impact tariffs have on infant industries, current account deficits, unemployment, or inequality, which remain open empirical questions. Trade economists have only begun to understand how multiproduct retailers affect who ultimately pays tariffs and still are struggling to meaningfully model unemployment in a tractable way conducive to fast or uniform application to policy analysis, while macro approaches overlook sectoral complexity. The field’s understanding of the importance of endogenous capital investment is growing, but it has not internalized the importance of the same intertemporal trade-offs between savings and consumption for assessing the distributional impacts of trade on households. Dispersion across assessments of the impacts of the U.S.–China trade war illustrates the frontiers that economists face assessing the macroeconomic impacts of tariffs.
Article
William Quinn and John Turner
Financial bubbles constitute some of history’s most significant economic events, but academic research into the phenomenon has often been narrow, with an excessive focus on whether bubble episodes invalidate or confirm the efficient markets hypothesis. The literature on the topic has also been somewhat siloed, with theoretical, experimental, qualitative, and quantitative methods used to develop relatively discrete bodies of research.
In order to overcome these deficiencies, future research needs to move beyond the rational/irrational dichotomy and holistically examine the causes and consequences of bubbles. Future research in financial bubbles should thus use a wider range of investigative tools to answer key questions or attempt to synthesize the findings of multiple research programs.
There are three areas in particular that future research should focus on: the role of information in a bubble, the aftermath of bubbles, and possible regulatory responses. While bubbles are sometimes seen as an inevitable part of capitalism, there have been long historical eras in which they were extremely rare, and these eras are likely to contain lessons for alleviating the negative effects of bubbles in the 21st century. Finally, the literature on bubbles has tended to neglect certain regions, and future research should hunt for undiscovered episodes outside of Europe and North America.
Article
Jevan Cherniwchan and M. Scott Taylor
Considerable progress has been made in understanding the relationship between international trade and the environment since Gene Grossman and Alan Krueger published their now seminal working paper examining the potential environmental effects of the North American Free Trade Agreement in 1991. Their work articulated a simple framework through which international trade and economic growth could affect the environment by impacting: the scale of economic activity (the scale effect), the composition of production across industries (the composition effect), or the emission intensity of individual industries (the technique effect). GK provided preliminary evidence of the relative magnitudes of the scale, composition and technique effects, and reached a striking conclusion: international trade would not necessarily harm the environment.
Much of the subsequent literature examining the effects of international trade and the environment has adopted Grossman and Krueger’s simple framework and builds directly from their initial foray into the area. We now have better empirical evidence of the relationship between economic growth and environmental quality, of how environmental regulations affect international trade and investment flows, and of the relative magnitudes of the scale, composition and technique effects.
Yet, the need for further progress remains along three key fronts. First, despite significant advances in our understanding of how economic growth affects environmental quality, evidence of the interaction between international trade, economic growth, and environmental outcomes remains scarce. Second, while a growing body of evidence suggests that environmental regulations significantly alter trade flows, it is still unclear if these policies have a larger or smaller effect than traditional determinants of comparative advantage. Third, although it is clear the technique effect is the primary driver of changes in pollution, evidence as to how trade has contributed to the technique effect is limited. Addressing these Three Remaining Challenges is necessary for assessing whether Grossman and Krueger’s conclusion that international trade need not necessarily harm the environment still holds today.
Article
Johannes Brumm, Christopher Krause, Andreas Schaab, and Simon Scheidegger
Solving dynamic economic models that capture salient real-world heterogeneity and nonlinearity requires the approximation of high-dimensional functions. As their dimensionality increases, compute time and storage requirements grow exponentially. Sparse grids alleviate this curse of dimensionality by substantially reducing the number of interpolation nodes, that is, grid points needed to achieve a desired level of accuracy. The construction principle of sparse grids is to extend univariate interpolation formulae to the multivariate case by choosing linear combinations of tensor products in a way that reduces the number of grid points by orders of magnitude relative to a full tensor-product grid and doing so without substantially increasing interpolation errors. The most popular versions of sparse grids used in economics are (dimension-adaptive) Smolyak sparse grids that use global polynomial basis functions, and (spatially adaptive) sparse grids with local basis functions. The former can economize on the number of interpolation nodes for sufficiently smooth functions, while the latter can also handle non-smooth functions with locally distinct behavior such as kinks. In economics, sparse grids are particularly useful for interpolating the policy and value functions of dynamic models with state spaces between two and several dozen dimensions, depending on the application. In discrete-time models, sparse grid interpolation can be embedded in standard time iteration or value function iteration algorithms. In continuous-time models, sparse grids can be embedded in finite-difference methods for solving partial differential equations like Hamilton-Jacobi-Bellman equations. In both cases, local adaptivity, as well as spatial adaptivity, can add a second layer of sparsity to the fundamental sparse-grid construction. Beyond these salient use-cases in economics, sparse grids can also accelerate other computational tasks that arise in high-dimensional settings, including regression, classification, density estimation, quadrature, and uncertainty quantification.
Article
Alessandra Bonfiglioli, Rosario Crinò, and Gino Gancia
International trade is dominated by a small number of very large firms. Models of trade with heterogeneous firms have been developed to study the causes and consequences of this observation. The canonical model of trade with heterogeneous firms shows that trade leads to between-firm reallocations and selection: It shifts employment toward firms with the best attributes and forces marginal firms to exit. The model also illustrates the role of heterogeneity, and its various sources, in explaining the volume of trade and the firm-level margins of adjustment. Consistent with the model, the empirical literature has documented that exporting is a rare activity, that exporting firms are larger and more productive than other firms, and that trade liberalization reallocates market shares toward the best-performing firms in various countries. Studies using transaction-level data have unveiled additional salient features of trade flows. First, sales by foreign firms are very heterogeneous and highly concentrated. Second, both the extensive margin (number of exporting firms) and the intensive margin (average export per firm) are important in explaining the level of exports and its changes over time. More heterogeneity in sales across firms is associated with a higher volume of trade along both margins. Third, increased foreign competition reallocates market shares toward top firms and hence can increase concentration from any country of origin. Numerous extensions of the benchmark model have been proposed to study other important aspects, such as the relevance of multi-product and multinational firms, the import behavior of firms, and the extent to which heterogeneity is endogenous to firms’ choices, but some open challenges still remain.
Article
Moussa P. Blimpo, Admasu Asfaw Maruta, and Josephine Ofori Adofo
Well-functioning institutions are essential for stable and prosperous societies. Despite significant improvement during the past three decades, the consolidation of coherent and stable institutions remains a challenge in many African countries. There is a persistent wedge between the de jure rules, the observance of the rules, and practices at many levels. The wedge largely stems from the fact that the analysis and design of institutions have focused mainly on a top-down approach, which gives more prominence to written laws. During the past two decades, however, a new strand of literature has emerged, focusing on accountability from the bottom up and making institutions more responsive to citizens’ needs. It designs and evaluates a mix of interventions, including information provision to local communities, training, or outright decentralization of decision-making at the local level. In theory, accountability from the bottom up may pave the way in shaping the institutions’ nature at the top—driven by superior localized knowledge. The empirical findings, however, have yielded a limited positive impact or remained mixed at best. Some of the early emerging regularities showed that information and transparency alone are not enough to generate accountability. The reasons include the lack of local ownership and the power asymmetry between the local elites and the people. Some of the studies have addressed many of these constraints at varying degrees without much improvement in the outcomes. A simple theoretical framework with multiple equilibria helps better understand this literature. In this framework, the literature consists of attempts to mobilize, gradually or at once, a critical mass to shift from existing norms and practices (inferior equilibrium) into another set of norms and practices (superior equilibrium). Shifting an equilibrium requires large and/or sustained shocks, whereas most interventions tend to be smaller in scope and short-lived. In addition, accountability at the bottom is often neglected relative to rights. If norms and practices within families and communities carry similar features as those observed at the top (e.g., abuse of one’s power), then the core of the problem is beyond just a wedge between the ruling elite and the citizens.
Article
Gianluca Cubadda and Alain Hecq
Reduced rank regression (RRR) has been extensively employed for modelling economic and financial time series. The main goals of RRR are to specify and estimate models that are capable of reproducing the presence of common dynamics among variables such as the serial correlation common feature and the multivariate autoregressive index models. Although cointegration analysis is likely the most prominent example of the use of RRR in econometrics, a large body of research is aimed at detecting and modelling co-movements in time series that are stationary or that have been stationarized after proper transformations. The motivations for the use of RRR in time series econometrics include dimension reductions, which simplify complex dynamics and thus make interpretations easier, as well as the pursuit of efficiency gains in both estimation and prediction. Via the final equation representation, RRR also makes the nexus between multivariate time series and parsimonious marginal ARIMA (autoregressive integrated moving average) models. RRR’s drawback, which is common to all of the dimension reduction techniques, is that the underlying restrictions may or may not be present in the data.
