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This chapter sheds new light on the linkages between stock market fluctuations and business cycles in Asia. It shows that at cyclical frequencies stock markets lead business cycles by six months on average. China, Korea, and Taiwan constitute exceptions, as their real and stock market cycles are contemporaneously synchronized. The low level of maturity of these markets offers a potential explanation of this outcome. Furthermore, we find that the linkage also holds during phases of cyclical upswing and downturn, with the exception of China, where the financial market lags behind industrial production during expansions. Finally, for most of the countries (except Thailand and Malaysia), the linkage is also robust to the presence of financial crises.

Relying on Clive Granger's many and varied contributions to econometric analysis, this paper considers some of the key econometric considerations involved in estimating Taylor-type rules for US data. We focus on the roles of unit roots, cointegration, structural breaks, and non-linearities to make the case that most existing estimates are based on an unbalanced regression. A variety of estimates reveal that neglected cointegration results in the omission of a necessary error correction term and that Federal Reserve (Fed) reactions during the Greenspan era appear to have been asymmetric. We argue that error correction and non-linearities may be one way to estimate Taylor rules over long samples when the underlying policy regime may have changed significantly.

Recently, star variables and the post-crisis nature of cyclical fluctuations have attracted a great deal of interest. In this chapter, the authors investigate different methods of assessing business cycles (BCs) for the European Union in general and the euro area in particular. First, the authors conduct a Monte Carlo (MC) experiment using a broad spectrum of univariate trend-cycle decomposition methods. The simulation aims to examine the ability of the analysed methods to find the observed simulated cycle with structural properties similar to actual macroeconomic data. For the simulation, the authors used the structural model’s parameters calibrated to the euro area’s real gross domestic product (GDP) and unemployment rate. The simulation outcomes indicate the sufficient composition of the suite of models (SoM) consisting of popular Hodrick–Prescott, Christiano–Fitzgerald and structural trend-cycle-seasonal filters, then used for the real application. The authors find that: (i) there is a high level of model uncertainty in comparing the estimates; (ii) growth rate (acceleration) cycles have often the worst performances, but they could be useful as early-warning predictors of turning points in growth and BCs; and (iii) the best-performing MC approaches provide a reasonable combination as the SoM. When swings last less time and/or are smaller, it is easier to pick a good alternative method to the suite to capture the BC for real GDP. Second, the authors estimate the BCs for real GDP and unemployment data varying from 1995Q1 to 2020Q4 (GDP) or 2020Q3 (unemployment), ending up with 28 cycles per country. This analysis also confirms that the BCs of euro area members are quite synchronized with the aggregate euro area. Some major differences can be found, however, especially in the case of periphery and new member states, with the latter improving in terms of coherency after the global financial crisis. The German cycles are among the cyclical movements least synchronized with the aggregate euro area.

Empirical work in macroeconomics almost universally relies on the hypothesis of rational expectations (RE).

This chapter departs from the literature by considering a variety of alternative expectations formation models. We study the econometric properties of a popular New Keynesian monetary DSGE model under different expectational assumptions: the benchmark case of RE, RE extended to allow for “news” about future shocks, near-RE and learning, and observed subjective expectations from surveys.

The results show that the econometric evaluation of the model is extremely sensitive to how expectations are modeled. The posterior distributions for the structural parameters significantly shift when the assumption of RE is modified. Estimates of the structural disturbances under different expectation processes are often dissimilar.

The modeling of expectations has important effects on the ability of the model to fit macroeconomic time series. The model achieves its worse fit under RE. The introduction of news improves fit. The best-fitting specifications, however, are those that assume learning. Expectations also have large effects on forecasting. Survey expectations, news, and learning all work to improve the model's one-step-ahead forecasting accuracy. RE, however, dominate over longer horizons, such as one-year ahead or beyond.

Transportation plays a central role in facilitating economic activities across sectors and between regions and thus should be essential to business cycle research. In this chapter, we identify four coincident indicators representing different aspects of the transportation sector. Foremost among them is the index of transportation services output (TSI) presented in the previous chapter. Following the long-standing methodology of National Bureau of Economic Research (NBER) business cycle research, the other three indicators that we include are payroll, personal consumption and employment – all pertaining to the transportation sector. Using a composite of the four indicators, we define the classical business cycle and growth cycle chronologies for the transportation sector. We find that, relative to the economy, business cycles in the transportation sector have an average lead of nearly 6 months at peaks and an average lag of 2 months at troughs. Similar to transportation business cycles, growth slowdowns in this sector also last longer than the economy-wide slowdowns by a few months. This study underscores the importance of transportation indicators in monitoring cyclical movements in the aggregate economy.

The role of trend inflation shocks for the U.S. macroeconomic dynamics is investigated by estimating two DSGE models of the business cycle. Policymakers are assumed to be concerned with a time-varying inflation target, which is modeled as a persistent and stochastic process. The identification of trend inflation shocks (as opposed to a number of alternative innovations) is achieved by exploiting the measure of trend inflation recently proposed by Aruoba and Schorfheide (2011). Our main findings point to a substantial contribution of trend inflation shocks for the volatility of inflation and the policy rate. Such contribution is found to be time dependent and highest during the mid-1970s to mid-1980s.

Introduction: The Great Financial Crisis of 2008 highlighted the importance of financial cycle fluctuations. While the regulatory response was to mandate higher bank capital requirements during the financial cycle upswing, academic research focussed on identifying the best performing early warning indicators to forecast financial cycle fluctuations that have proven to be often unrelated to business cycle changes. To safeguard the global financial system against the financial cycle fluctuations, Basel Committee of Banking Supervisors, based on first strands of empirical evidence, proposed the credit-to-GDP gap as the headline indicator tied to the countercyclical capital buffer. However, later research on this indicator identified certain concerns, among them subpar performance for economies with short available data series.

Aim of the Study: To this end this study aims to analyse various financial cycle indicators from a unique perspective of their potential viability under limited historical data availability.

Methods: For this purpose, a meta-study of existing research is carried out as well as an empirical study to compare performance of certain indicators for the sample of six countries in the Central, Eastern and South-Eastern European region, where long data series are not available.

Main Findings: It was found that certain approaches, among them calculation of raw credit growth rate and application of Hamilton filter, can supplement or possibly even outperform the Basel credit-to-GDP gap indicator under limited data availability.

Conclusion: Author concludes that for limited time series Basel credit-to-GDP gap can be potentially outperformed by other indicators and further research in this currently under-studied field is warranted.

Originality of the Paper: By using various financial cycle indicators that already proven their early warning prediction powers from previous research, this study focusses on their potential viability under limited historical data availability. Respective findings might be appreciated for supplementing policy-makers’ toolkits as complementary indicators in cases where there is no available long time series for financial cycle estimation, for example, such as countries that entered market economies relatively late.

Several official institutions (NBER, OECD, CEPR, and others) provide business cycle chronologies with lags ranging from three months to several years. In this paper, we propose a Markov-switching dynamic factor model that allows for a more timely estimation of turning points. We apply one-step and two-step estimation approaches to French data and compare their performance. One-step maximum likelihood estimation is confined to relatively small data sets, whereas two-step approach that uses principal components can accommodate much bigger information sets. We find that both methods give qualitatively similar results and agree with the OECD dating of recessions on a sample of monthly data covering the period 1993–2014. The two-step method is more precise in determining the beginnings and ends of recessions as given by the OECD. Both methods indicate additional downturns in the French economy that were too short to enter the OECD chronology.