Leading and Coincident Indicators

“Coincident and Leading Indicators of the Stock Market,” with S. Potter, Journal of Empirical Finance, Vol. 7, No. 1, 87-111, 2000. (Download Repec or CoinleadSM)

Abstract: In this paper we have two goals: first, we want to represent monthly stock market fluctuations by constructing a non-linear coincident financial indicator. The indicator is constructed as an unobservable factor whose first moment and conditional volatility are driven by a two-state Markov variable. It can be interpreted as the investors’ real-time belief about the state of financial conditions. Second, we want to explore an approach in which investors may use their perceptions of the state of the economy to form forecasts of financial market conditions and possibly of excess returns. To investigate this, we build leading indicators as forecasts of the estimated coincident financial index. The leading indicators yield better within and out-of-sample performance in forecasting, not only the state of the stock market but also of excess stock returns, as compared with the performance obtained using linear methods that have been proposed in the existing literature.

"Leading Indicators of Country Risk and Currency Crises “ The Asian Experience,” with Fang Dong , Economic Review, Federal Reserve Bank of Atlanta, First Quarter, Vol. 89, No. 1, 26-37, 2004. (Download Repec or CountryRisk)

Abstract: 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.

"Leading Indicators of the Capital Goods Industry in Brazil," with Igor Morais, Brazilian Review of Econometrics, Vol. 31, No. 1, 137-171, 2011.  Recipient of Honor Award from the National Confederation of Industries of Brazil  - Confederacao Nacional das Industrias, CNI. (Download LeadingCapital)  

Abstract: The goal of this paper is to build leading indicators to predict the capital goods business cycle in Brazil. We propose a probit model with autoregressive dynamics consisting of series with predictive power to anticipate contractions in this sector. The model is especially suitable for this sector as it includes information about the characteristics of cycle phases and their duration in the estimation of recession probability, and adapts well to the high volatility of the capital goods business cycle. The results indicate that the dynamic probit model has a better forecasting performance than the simple probit model in several aspects, both within and out of sample and in real time. 

“A Monthly Indicator of Brazilian GDP,” in Brazilian Review of Econometrics Vol. 21, No. 1, 1-15, 2001 – Recipient of Honor Award from the Brazilian Econometric Society. (Download Repec or BrazilIndicatorGDP)

Abstract: This paper constructs an indicator of Brazilian GDP at the monthly frequency. The peculiar instability and abrupt changes of regimes in the dynamic behavior of the Brazilian business cycle were explicitly modeled within nonlinear frameworks. In particular, a Markov switching dynamic factor model was used to combine several macroeconomic variables that display simultaneous comovements with aggregate economic activity. The model generates as output a monthly indicator of the Brazilian GDP and real time probabilities of the current phase of the Brazilian business cycle. The monthly indicator shows a remarkable historical conformity with cyclical movements of GDP. In addition, the estimated filtered probabilities predict all recessions in sample and out-of-sample. The ability of the indicator in linear forecasting growth rates of GDP is also examined. The estimated indicator displays a better in-sample and out-of-sample predictive performance in forecasting growth rates of real GDP, compared to a linear autoregressive model for GDP. These results suggest that the estimated monthly indicator can be used to forecast GDP and to monitor the state of the Brazilian economy in real time.

“Leading Indicators of the Brazilian Inflation,” Economic Research and Planning (Pesquisa e Planejamento Economico), Vol. 31, 1, 323-354, 2001. (Download Repec - English or BrazilLeadingInflation-Portuguese).

Abstract: 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.