Leading Indicators of Inflation for Brazil

The goal of this project is to construct leading indicators that anticipate inflation cycle turning points on a real time monitoring basis. As a first step, turning points of the IPCA inflation are determined using a periodic stochastic Markov switching model. These turning points are the event timing that the leading indicators should anticipate. A dynamic factor model is then used to extract common cyclical movements in a set of variables that display predictive content for inflation. The leading indicators are designed to serve as practical tools to assist real-time monitoring of monetary policy on a month-to-month basis. Thus, the indicators are built and ranked according to their out-of-sample forecasting performance. The leading indicators are found to be an informative tool for signaling future phases of the inflation cycle out-of-sample, even in real time when only preliminary and unrevised data are available.

International Financial Aggregation and Index Number Theory: A Chronological Half-Century Empirical Overview.

This paper comprises a survey of a half century of research on international monetary aggregate data. We argue that since monetary assets began yielding interest, the simple sum monetary aggregates have had no foundations in economic theory and have sequentially produced one source of misunderstanding after another. The bad data produced by simple sum aggregation have contaminated research in monetary economics, have resulted in needless “paradoxes,” and have produced decades of misunderstandings in international monetary economics research and policy. While better data, based correctly on index number theory and aggregation theory, now exist, the official central bank data most commonly used have not improved in most parts of the world. While aggregation theoretic monetary aggregates exist for internal use at the European Central Bank, the Bank of Japan, and many other central banks throughout the world, the only central banks that currently make aggregation theoretic monetary aggregates available to the public are the Bank of England and the St. Louis Federal Reserve Bank. No other area of economics has been so seriously damaged by data unrelated to valid index number and aggregation theory. In this paper we chronologically review the past research in this area and connect the data errors with the resulting policy and inference errors. Future research on monetary aggregation and policy can most advantageously focus on extensions to exchange rate risk and its implications for multilateral aggregation over monetary asset portfolios containing assets denominated in more than one currency. The relevant theory for multilateral aggregation with exchange rate risk has been derived by Barnett (2007) and Barnett and Wu (2005).Measurement error, monetary aggregation, Divisia index, aggregation, monetary policy, index number theory, exchange rate risk, multilateral aggregation, open economy monetary economics.

Identifying business cycle turning points in real time

This paper evaluates the ability of a statistical regime-switching model to identify turning points in U.S. economic activity in real time. The authors work with Markov-switching models of real GDP and employment that, when estimated on the entire post-war sample, provide a chronology of business cycle peak and trough dates very close to that produced by the National Bureau of Economic Research (NBER). Next, they investigate how accurately and quickly the models would have identified turning points had they been used in real-time for the past forty years. In general, the models identify turning point dates in real-time that are close to the NBER dates. For both business cycle peaks and troughs, the models provide systematic improvement over the NBER in the speed at which turning points are identified. Importantly, the models achieve this with few instances of "false positives." Overall, the evidence suggests that the regime-switching model could be a useful supplement to the NBER Business Cycle Dating Committee for establishing turning point dates. The model appears to capture the features of the NBER chronology in an accurate, timely way, and does so in a transparent and consistent fashion.Forecasting ; Economic conditions ; Business cycles

Employment and the business cycle

The Great Recession of 2007-2009 has not only caused a large wealth loss, it was also followed by a sluggish subsequent recovery. Two years after officially emerging from the recession, the economy was still growing at a low pace and payroll employment was far from reaching its previous peak. However, assessment of the employment situation was markedly different across different series. The two most important employment series, payroll employment (ENAP) and civilian employment (TCE), have recently been displaying divergent patterns. This has been a source of great uncertainty regarding labor market conditions. This paper investigates the differences in the cyclical dynamics of these series and the implications for monitoring business cycle on a current basis. Univariate and multivariate Markov switching models are applied to revised and real time unrevised data. We find that the main differences across these series occur around recessions. The employment measures have diverged considerably around the last three recessions in 1990-1991, in 2001, and in 2007-2009, but especially during their subsequent recoveries. In particular, while the probabilities of recession for models that include ENAP depict jobless recoveries, the probabilities of recessions from models with TCE fall right around the trough of the last three recessions, as determined by the NBER. This significantly impacts the identification of turning points in multivariate models in sample and in recursive real time analysis, with models that use TCE being more accurate compared to the NBER dating, and delivering faster call of troughs in real time. Models that include ENAP series, on the other hand, yield delays in signaling business cycle troughs, especially the most recent ones.Employment, Business Cycle, Turning Point, Real Time, Markov-Switching, Dynamic Factor Model, Jobless Recovery

Leading indicators of country risk and currency crises: the Asian experience

Most emerging capital markets in recent years adopted a system that narrowly pegs their currencies’ exchange rates to the U.S. dollar. While such a system has a number of advantages, it makes a country vulnerable to shocks in mobile international capital markets and can lead to reactive strategies that can drive the country into a currency crisis and inflationary recession. ; This article aims to construct an early warning system for international currency crises using financial variables reflecting investors’ expectations and banking distress, which are highly sensitive to changes in the economic environment. The authors use a dynamic factor model that switches between two regimes—representing periods of relative calmness and periods prone to currency crises—to construct leading indicators of country risk and currency crises. ; The method is applied to evaluate the model’s in-sample and out-of-sample performance in anticipating currency crises in the last two decades in Thailand, Indonesia, and Korea. The model successfully produces early signals of these crises, particularly the most severe one, which occurred in 1997. ; The study’s success in signaling future currency crises in real time demonstrates that the model’s “country risk” indicators can be informative tools that allow central banks to take preemptive counterpolicy measures to avoid a crisis or mitigate its severity.

Monitoring Business Cycles with Structural Breaks

This paper examines the predictive content of coincident variables for monitoring U.S. recessions in the presence of instabilities. We propose several specifications of a probit model for classifying phases of the business cycle. We find strong evidence in favor of the ones that allow for the possibility that the economy has experienced recurrent breaks. The recession probabilities of these models provide a clearer classification of the business cycle into expansion and recession periods, and superior performance in the ability to correctly call recessions and to avoid false recession signals. Overall, the sensitivity, specificity, and accuracy of these models are far superior as well as their ability to timely signal recessions. The results indicate the importance of considering recurrent breaks for monitoring business cycles.Recession, Instability, Bayesian Methods, Probit model, Breaks.

A Joint Dynamic Bi-Factor Model of the Yield Curve and the Economy as a Predictor of Business Cycles

This paper proposes an econometric model of the joint dynamic relationship between the yield curve and the economy to predict business cycles. We examine the predictive value of the yield curve to forecast both future economic growth as well as the beginning and end of economic recessions at the monthly frequency. The proposed multivariate dynamic factor model takes into account not only the popular term spread but also information extracted from the entire yield curve. The nonlinear model is used to investigate the interrelationship between the phases of the bond market and of the business cycle. The results indicate a strong interrelation between these two sectors. Although the popular term spread has a reasonable forecasting performance, the proposed factor model of the yield curve exhibits substantial incremental predictive value. This result holds in-sample and out-of-sample, using revised or real time unrevised data.Forecasting, Business Cycles, Yield Curve, Dynamic Factor Models, Markov Switching.

Microfoundations of Inflation Persistence in the New Keynesian Phillips Curve

This paper proposes a dynamic stochastic general equilibrium model that endogenously generates inflation persistence. We assume that although firms change prices periodically, they face convex costs that preclude optimal adjustment. In essence, the model assumes that price stickiness arises from both the frequency and size of price adjustments. The model is estimated using Bayesian techniques and the results strongly support both sources of price stickiness in the U.S. data. In contrast with traditional sticky price models, the framework yields inflation inertia, delayed effect of monetary policy shocks on inflation, and the observed "reverse dynamic" correlation between inflation and economic activity.In Inflation Persistence, Phillips Curve, Sticky Prices, Convex Costs

Dating Business Cycle Turning Points

This paper discusses formal quantitative algorithms that can be used to identify business cycle turning points. An intuitive, graphical derivation of these algorithms is presented along with a description of how they can be implemented making very minimal distributional assumptions. We also provide the intuition and detailed description of these algorithms for both simple parametric univariate inference as well as latent-variable multiple-indicator inference using a state-space Markov-switching approach. We illustrate the promise of this approach by reconstructing the inferences that would have been generated if parameters had to be estimated and inferences drawn based on data as they were originally released at each historical date. Waiting until one extra quarter of GDP growth is reported or one extra month of the monthly indicators released before making a call of a business cycle turning point helps reduce the risk of misclassification. We introduce two new measures for dating business cycle turning points, which we call the %u201Cquarterly real-time GDP-based recession probability index%u201D and the %u201Cmonthly real-time multiple-indicator recession probability index%u201D that incorporate these principles. Both indexes perform quite well in simulation with real-time data bases. We also discuss some of the potential complicating factors one might want to consider for such an analysis, such as the reduced volatility of output growth rates since 1984 and the changing cyclical behavior of employment. Although such refinements can improve the inference, we nevertheless find that the simpler specifications perform very well historically and may be more robust for recognizing future business cycle turning points of unknown character.