Evaluating currency crises: A Bayesian Markov switching approach

In this paper we examine the nature of a currency crisis. We do so by employing an out-of-sample forecasting exercise to analyse the Mexican crisis in 1994. Forecast evaluation was based on modern econometric techniques concerning the shape of forecaster’s loss function. We also extend the empirical framework suggested by Jeanne and Masson [Jeanne, O., Masson, P., 2000. Currency crises and Markov-switching regimes. Journal of International Economics 50, 327–350] to test for the hypothesis that the currency crisis was driven by sunspots. To this end we contribute to the existing literature by comparing Markov regime switching model with a time-varying transition probabilities with two alternative models. The first is a Markov regime switching model with constant transition probabilities. The second is a linear benchmark model. Empirical results show that the proxy for the probability of devaluation is an important factor explaining the nature of currency crisis. More concretely, when the expectation market pressure was used as a proxy of probability of devaluation, forecast evaluation supports the view that currency crisis was driven by market expectation unrelated to fundamentals. Alternatively, when interest rate differential is used as a proxy for probability of devaluation, currency crisis was due to predictable deterioration of fundamentals.     

Power issues when testing the Markov switching model with the sup likelihood ratio test using U.S. output

The likelihood ratio (LR) test statistic for the test of a linear AR(1) model against the alternative of a Markov switching model does not possess the standard χ² distribution. Garcia (1998) derives the asymptotic distribution of the Sup LR test statistic under these non-standard conditions allowing the researcher to easily compare the two models. This paper examines the power properties of this test statistic using Monte Carlo experiments calibrated to U.S. output growth data. The results suggest a test of reasonable power. When the experiments are calibrated to annual data, power is 82% at 200 observations. When the experiments are calibrated to quarterly data power is 57% for the same sample size. First Version Received: March 2000/Final Version Received: March 2001     

Interest Rate Volatility Regimes in Selected Asian Countries: A Univariate Markov Switching Analysis

Business cycle dynamics are determined by relatively large volatilities in output,consumption,and investment,which leads to cyclical fluctuations in interest rates.Using the Markov switching model,we model the nominal interest rate movements to explain the volatility regime shifts in a set of selected emerging Asian economies.The estimated results provide significant evidence of regime-dependent means,variances,and probabilities in both stable and volatile regimes in selected countries,confirming the existence of two distinct regimes in nominal interest rate movements.In addition,the smoothed probability results of switching autoregressive model show that the model is capable of capturing the two regimes for the corresponding nominal interest rate behaviors.Besides,the results reveal that the stables regimes have higher durations than the volatile regimes.This study also shows the advantage of Markov switching models over conventional regression models,allowing the identification of different regimes for the cyclical behavior of interest rates.     

Dynamics of regional unemployment rates in Brazil: Fractional behavior,structural breaks,and Markov switching

This paper investigates unemployment dynamics in Brazil and in its major metropolitan regions using a fractional integration model. Aspects regarding structural breaks and regime switches are discussed as well. To do that, the methods proposed by Hassler and Meller (2009) and Tsay and Härdle (2009) are used. The major results indicate that unemployment rates have two different levels of persistence. The first one is nonstationary whereas the second one is nonstationary but mean-reverting. Based on these findings, the convergence hypothesis of regional unemployment rates was tested. Following the fractional stochastic convergence criterion put forward by Mello and Guimaraes-Filho (2007), it was concluded that regional unemployment rates are convergent.     

Seasonality and Markov switching in an unobserved component time series model

It is generally acknowledged that the growth rate of output, the seasonal pattern, and the business cycle are best estimated simultaneously. To achieve this, we develop an unobserved component time series model for seasonally unadjusted US GDP. Our model incorporates a Markov switching regime to produce periods of expansion and recession, both of which are characterized by different underlying growth rates. Although both growth rates are time-varying, they are assumed to be cointegrated. The analysis is Bayesian, which fully accounts for all sources of uncertainty. Comparison with results from a similar model for seasonally adjusted data indicates that the seasonal adjustment of the data significantly alters several aspects of the full model. First Version Received: January 2001/Final Version Received: February 2002 Send offprint requests to: Rob Luginbuhl?Correspondence to: Rob Luginbuhl     

The business cycle in a financially deregulated context: Theory and evidence

This article proposes Minsky's financial instability hypothesis (FIH) as a theoretical underpinning for a three‐regime business cycles model. Further, it is argued that the development of the FIH for open, developing economies (FIH‐ODE) provides a better understanding of the performance of business cycles in these economies, particularly during the last two decades. In support of these claims, a three‐regime autoregressive Markov switching model is estimated from 1980q1 to 2000q4 to Mexico's quarterly real GDP to investigate its business cycle behaviour. The estimated probabilities of the high and medium growth regimes suggest, for example, that after the financial liberalisation programme was fully launched, in the late 1980s, the economy shifted from the regime of medium to high growth (and vice versa) swiftly, reflecting its dependence on capital flows. Furthermore, the estimated parameters indicate that the average length of the business cycle has not changed.