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Over time, the reference cycle of an economy is determined by a sequence of non-observable business cycle turning points involving a partition of the time calendar into non-overlapping episodes of expansions and recessions. Dating these turning points helps develop economic analysis and is useful for economic agents, whether policymakers, investors, or academics. Aiming to be transparent and reproducible, determining the reference cycle with statistical frameworks that automatically date turning points from a set of coincident economic indicators has been the source of remarkable advances in this research context. These methods can be classified into different broad sets of categories. Depending on the assumptions made in the data-generating process, the dating methods are parametric and non-parametric. There are two main approaches to dealing with multivariate data sets: average then date and date then average. The former approach focuses on computing a reference series of the aggregate economy, usually by averaging the indicators across the cross-sectional dimension. Then, the global turning points are dated on the aggregate indicator using one of the business cycle dating models available in the literature. The latter approach consists of dating the peaks and troughs in a set of coincident business cycle indicators separately, assessing the reference cycle itself in those periods where the individual turning points cohere. In the early 21st century, literature has shown that future work on dating the reference cycle will require dealing with a set of challenges. First, new tools have become available, which, being increasingly sophisticated, may enlarge the existing academic–practitioner gap. Compiling the codes that implement the dating methods and facilitating their practical implementation may reduce this gap. Second, the pandemic shock hitting worldwide economies led most industrialized countries to record 2020’s most significant fall and the largest rebound in national economic indicators since records began. Under these influential observations, the outcomes of dating methods could misrepresent the actual reference cycle, especially in the case of parametric approaches. Exploring non-parametric approaches, big data sources, and the classification ability offered by machine learning methods could help improve dating analyses’ performance.

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.

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.