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This article proposes a new approach to the analysis of the reference cycle turning points, defined on the basis of the specific turning points of a broad set of coincident economic indicators. Each individual pair of specific peaks and troughs from these indicators is viewed as a realization of a mixture of an unspecified number of separate bivariate Gaussian distributions whose different means are the reference turning points. These dates break the sample into separate reference cycle phases, whose shifts are modeled by a hidden Markov chain. The transition probability matrix is constrained so that the specification is equivalent to a multiple change-point model. Bayesian estimation of finite Markov mixture modeling techniques is suggested to estimate the model. Several Monte Carlo experiments are used to show the accuracy of the model to date reference cycles that suffer from short phases, uncertain turning points, small samples, and asymmetric cycles. In the empirical section, we show the high performance of our approach to identifying the US reference cycle, with little difference from the timing of the turning point dates established by the NBER. In a pseudo real-time analysis, we also show the good performance of this methodology in terms of accuracy and speed of detection of turning point dates.

This paper evaluates the consequences of the financial and sovereign debt crises on the evolution of the business cycle synchronization across all the Euro Area members. We take advantage of the dimension reduction properties of dynamic factor models to summarize a large dataset of macroeconomic indicators for the Euro Area countries. Then, we estimate latent state variables based on Markov-switching methodologies to obtain a time-varying measure of business cycle synchronization. The combination of these two techniques allows us to describe the evolution in the degree of coincidence of the business cycle phases along time for this set of countries. Our results suggest that there was a general decline in the degree of business cycle synchronization across the Euro Area countries following the financial and the sovereign debt crises. Although they have recovered the levels of business cycle synchronization exhibited before these events, there are significant differences across countries in the required time to recover those levels.

We use a local projection approach to analyze the effect of economic recessions on income inequality in a comprehensive sample of 43 countries from 1960 to 2016. Although we consider both business-cycle and growth-cycle recessions, we fail to find evidence of significant positive impacts of economic downturns on income distribution, once controls are added to the model. However, we do find important differences across countries, which mainly depend on the degree of economic development.

We extend the Markov-switching dynamic factor model to account for some of the specificities of the day-to-day monitoring of economic developments from macroeconomic indicators, such as mixed sampling frequencies and ragged-edge data. First, we evaluate the theoretical gains of using data that are available promptly for computing probabilities of recession in real time. Second, we show how to estimate the model that deals with unbalanced panels of data and mixed frequencies, and examine the benefits of this extension through several Monte Carlo simulations. Finally, we assess its empirical reliability for the computation of real-time inferences of the US business cycle, and compare it with the alternative method of forecasting the probabilities of recession from balanced panels.

This paper examines the evolution of the distribution of industry-specific business cycle linkages, which are modeled through a multivariate Markov-switching model and estimated by Gibbs sampling. Using nonparametric density estimation approaches, we find that the number and location of modes in the distribution of industrial dissimilarities change over the business cycle. There is a relatively stable trimodal pattern during expansionary and recessionary phases characterized by highly, moderately, and lowly synchronized industries. However, during phase changes, the density mass spreads from moderately synchronized industries to lowly synchronized industries. This agrees with a sequential transmission of the industrial business cycle dynamics.

This paper analyzes the evolution of growth cycles and business cycles in Latin America from 1980 to 2013 by using monthly industrial production. Focusing on both synchronization and other cyclical features, we find evidence of significant cyclical links between the countries of the region, which seem to be highly integrated in this period. Notably, we find that the Great Recession did not lead to any significant impact on the preexisting Latin American cyclical linkages.

We examine the finite-sample performances of dynamic factor models that use either aggregate or disaggregate data, where the latter rely on finer disaggregations of the headline concepts of a small set of economic categories. Our Monte Carlo analysis reveals that the use of the series with the largest averaged within-category correlations outperforms the use of disaggregate data for factor estimation and forecasting in several cases. This occurs for high levels of cross-correlation across the idiosyncratic errors of series that belong to the same category, for oversampled categories, and especially for high levels of persistence in either the common factor or the idiosyncratic errors. However, the forecasting gains are reduced considerably when the target series are persistent. This could potentially explain why there is no clear ranking between the aggregate and disaggregate approaches when using the constituent balanced panel of the Stock-Watson factor model, which classifies the US data into 13 economic categories.

We advocate a dynamic factor model to provide alternative measures of output data using indirect information from economic indicators. We apply the method to show evidence of a significant gap between estimated and official measures of Argentine GDP since 2007.

We propose a small-scale dynamic factor model for monitoring Argentine gross domestic product (GDP) in real time using economic data at mixed frequencies (monthly and quarterly), which are published with different time lags. Our model not only produces a coincident index of the Argentine business cycle in striking accordance with professional consensus and the history of the Argentine business cycle, but also generates accurate short-run forecasts of the highly volatile Argentine GDP growth. By using a pseudo real-time empirical evaluation, we show that our model produces reliable backcasts, nowcasts, and forecasts well before the official data are released.

In order to perform real-time business cycle inferences and forecasts of GDP growth rates in the euro area, we use an extension of the Markov-switching dynamic factor models that accounts for the features of the day-to-day monitoring of economic developments, such as ragged edges, mixed frequencies and data revisions. We provide examples that show the nonlinear nature of the relationships between data revisions, point forecasts and forecast uncertainty. Based on our empirical results, we think that the real-time probabilities of recession inferred from the model are an appropriate statistic for capturing what the press call green shoots , and for monitoring double-dip recessions.

This paper extends the Markov-switching vector autoregressive models to accommodate both the typical lack of synchronicity that characterizes the real-time daily flow of macroeconomic information and economic indicators sampled at different frequencies. The results of the empirical application suggest that the model is able to capture the features of the NBER business cycle chronology very accurately.

Practitioners do not always use research findings, as the research is not always conducted in a manner relevant to real-world practice. This survey seeks to close the gap between research and practice in respect of short-term forecasting in real time. To this end, we review the most relevant recent contributions to the literature, examining their pros and cons, and we take the liberty of proposing some avenues of future research. We include bridge equations, MIDAS, VARs, factor models and Markov-switching factor models, all allowing for mixed-frequency and ragged ends. Using the four constituent monthly series of the Stock-Watson coincident index, industrial production, employment, income and sales, we evaluate their empirical performance to forecast quarterly US GDP growth rates in real time. Finally, we review the main results having regard to the number of predictors in factorbased forecasts and how the selection of the more informative or representative variables can be made.

I find that real US GDP is better characterized as a trend stationary Markov-switching process than as having a (regime-dependent) unit root. I examine the effects of both assumptions on the analysis of business cycle features and their implications for the persistence of the dynamic response of output to a random disturbance.

We propose a model to compute short-term forecasts of the Euro area GDP growth in real-time. To allow for forecast evaluation, we construct a real-time data set that changes for each vintage date and includes the exact information that was available at the time of each forecast. In this context, we provide examples that show how data revisions and data availability affect point forecasts and forecast uncertainty.

The authors propose a new panel data methodology to test real convergence in a non-linear framework. This extends the existing methods by combining three approaches: the threshold model, the panel data unit root tests, and the computation of critical values by bootstrap simulation. The authors apply their methodology to the per capita outputs of a total of 15 European countries, including some of the East European countries that have recently joined the EU.

This paper analyzes if each European country presents business cycles that are similar enough to validate what some authors call the European cycle. Contrary to the majority of papers on business cycles, we concentrate on the appearance of the cycle, not on the synchronization. We provide a robust methodology for dating and characterizing business cycles and their phases and adopt the model-based cluster analysis to test the existence of an unique cluster (a common cycle) against more than one. We find evidence against a common cycle. Finally, we find no clear relation between similarities in business cycle appearance and synchronization across countries.

Based on a novel extension of existing multivariate Markov-switching models, we provide the reader with a useful tool for analyzing current business conditions and making predictions about the future state of the Euro-area economy in real time. Apart from the Industrial Production Index, we find that the European Commission Industrial Confidence Indicator, which is issued with no delay, is very useful for constructing the real-time predictions.

I investigate cointegrating relationships such that, even though the long-run attractors are assumed to be linear, the dynamics of the equilibrium errors depends on the business cycle. I postulate a Markov-switching common stochastic trends model to study both the short-run responses to permanent shocks and the effects of recessions in the long-run growth. I apply these findings to explore the short- and long-run asymmetric relationships among output, consumption and investment.

Relationships between antimicrobial use and MRSA prevalence are analyzed in Aberdeen, Scotland., Similar to many hospitals worldwide, Aberdeen Royal Infirmary has had an outbreak of methicillin-resistant Staphylococcus aureus (MRSA). In this setting, the outbreak is attributable to two major clones. The relationships between antimicrobial use and MRSA prevalence were analyzed by time-series analysis. From June 1997 to December 2000, dynamic, temporal relationships were found between monthly %MRSA and previous %MRSA, macrolide use, third-generation cephalosporin use, and fluoroquinolone use. This study suggests that use of antimicrobial drugs to which the MRSA outbreak strains are resistant may be an important factor in perpetuating the outbreak. Moreover, this study confirmed the ecologic effect of antimicrobial drug use (i.e., current antimicrobial use) may have an effect on resistance in future patients. Although these results may not be generalized to other hospitals, they suggest new directions for control of MRSA, which has thus far proved difficult and expensive.

In this paper, I extend to a multiple-equation context the linearity, model selection and model adequacy tests recently proposed for univariate smooth transition regression models. Using this result, I examine the nonlinear forecasting power of the Conference Board composite index of leading indicators to predict both output growth and the business-cycle phases of the US economy in real time. Copyright © 2004 John Wiley & Sons, Ltd.

We use the Stock-Watson diffusion index methodology to summarize the information contained in a wide set of monthly series (published in the Statistical Bulletin of the Bank of Spain) by means of a reduced number of factors. We find that the first two factors may be used as indicators of the core inflation and the business cycle dynamics of the Spanish economy, respectively. In addition, we study the effects of incorporating large information sets for the analysis of monetary policy. Finally, we show that forecasting prices and output with our factors outperforms other standard alternative forecasting procedures.