Results 1  10
of
43
Has the U.S. Economy Become More Stable? A Bayesian Approach Based on a MarkovSwitching Model of Business Cycle
, 1999
"... We hope to be able to provide answers to the following questions: 1) Has there been a structural break in postwar U.S. real GDP growth toward more stabilization? 2) If so, when would it have been? 3) What's the nature of the structural break? For this purpose, we employ a Bayesian approach to dealin ..."
Abstract

Cited by 256 (13 self)
 Add to MetaCart
We hope to be able to provide answers to the following questions: 1) Has there been a structural break in postwar U.S. real GDP growth toward more stabilization? 2) If so, when would it have been? 3) What's the nature of the structural break? For this purpose, we employ a Bayesian approach to dealing with structural break at an unknown changepoint in a Markovswitching model of business cycle. Empirical results suggest that there has been a structural break in U.S. real GDP growth toward more stabilization, with the posterior mode of the break date around 1984:1. Furthermore, we #nd a narrowing gap between growth rates during recessions and booms is at least as important as a decline in the volatility of shocks. Key Words: Bayes Factor, Gibbs sampling, Marginal Likelihood, MarkovSwitching, Stabilization, Structural Break. JEL Classi#cations: C11, C12, C22, E32. 1. Introduction In the literature, the issue of postwar stabilization of the U.S. economy relative to the prewar period has...
Selection of estimation window in the presence of breaks
 Journal of Econometrics
, 2007
"... In situations where a regression model is subject to one or more breaks it is shown that it can be optimal to use prebreak data to estimate the parameters of the model used to compute outofsample forecasts. The issue of how best to exploit the tradeo that might exist between bias and forecast er ..."
Abstract

Cited by 35 (6 self)
 Add to MetaCart
In situations where a regression model is subject to one or more breaks it is shown that it can be optimal to use prebreak data to estimate the parameters of the model used to compute outofsample forecasts. The issue of how best to exploit the tradeo that might exist between bias and forecast error variance is explored and illustrated for the multivariate regression model under the assumption of strictly exogenous regressors. In practice when this assumption cannot be maintained and both the time and size of the breaks are unknown the optimal choice of the observation window will be subject to further uncertainties that make exploiting the biasvariance tradeo di cult. To that end we propose a new set of crossvalidation methods for selection of a single estimation window and weighting or pooling methods for combination of forecasts based on estimation windows of di erent lengths. Monte Carlo simulations are used to show when these procedures work well compared with methods that ignore the presence of breaks. JEL Classi cations: C22, C53.
Testing for Convergence Clubs in Income percapita: A Predictive Density Approach.
 International Economic Review
, 1999
"... The paper proposes a technique to jointly tests for groupings of unknown size in the cross sectional dimension of a panel and estimates the parametersofeach group, and applies it to identifying convergence clubs in income percapita.The approach uses the predictive densityofthedata, conditional o ..."
Abstract

Cited by 30 (2 self)
 Add to MetaCart
The paper proposes a technique to jointly tests for groupings of unknown size in the cross sectional dimension of a panel and estimates the parametersofeach group, and applies it to identifying convergence clubs in income percapita.The approach uses the predictive densityofthedata, conditional on the parameters of the model. The steady state distribution of European regional data clustersaround four polesofattraction with differenteconomic features. The distribution of income percapita of OECD countries has twopolesofattraction and each group has clearly identifiable economic characteristics. JEL Classification No.: C11, D90, O47 Key words: Heterogeneities, Panel Data, Predictive Density, Income Inequality. 3 Iwouldlike to thank three anonymous referees, the editor of this journal, Bruce Hansen, Hashem Pesaran, Russell Cooper, Christopher Croux, Albert Marcet and the participants atseminars at Universitat Pompeu Fabra, the University of Southampton, Universite de Paris IM...
Dealing with Structural Breaks
 IN PALGRAVE HANDBOOK OF ECONOMETRICS
, 2006
"... This chapter is concerned with methodological issues related to estimation, testing and computation in the context of structural changes in the linear models. A central theme of the review is the interplay between structural change and unit root and on methods to distinguish between the two. The top ..."
Abstract

Cited by 26 (7 self)
 Add to MetaCart
This chapter is concerned with methodological issues related to estimation, testing and computation in the context of structural changes in the linear models. A central theme of the review is the interplay between structural change and unit root and on methods to distinguish between the two. The topics covered are: methods related to estimation and inference about break dates for single equations with or without restrictions, with extensions to multiequations systems where allowance is also made for changes in the variability of the shocks; tests for structural changes including tests for a single or multiple changes and tests valid with unit root or trending regressors, and tests for changes in the trend function of a series that can be integrated or trendstationary; testing for a unit root versus trendstationarity in the presence of structural changes in the trend function; testing for cointegration in the presence of structural changes; and issues related to long memory and level shifts. Our focus is on the conceptual issues about the frameworks adopted and the assumptions imposed as they relate to potential applicability. We also highlight the potential problems that can occur with methods that are commonly used and recent work that has been done to overcome them.
How Costly is it to Ignore Breaks when Forecasting the Direction of a Time Series?
, 2003
"... Empirical evidence suggests that many macroeconomic and financial time series are subject to occasional structural breaks. In this paper we present analytical results quantifying the effects of such breaks on the correlation between the forecast and the realization and on the ability to forecast ..."
Abstract

Cited by 24 (3 self)
 Add to MetaCart
Empirical evidence suggests that many macroeconomic and financial time series are subject to occasional structural breaks. In this paper we present analytical results quantifying the effects of such breaks on the correlation between the forecast and the realization and on the ability to forecast the sign or direction of a timeseries that is subject to breaks. Our results suggest that it can be very costly to ignore breaks. Forecasting approaches that condition on the most recent break are likely to perform better over unconditional approaches that use expanding or rolling estimation windows provided that the break is reasonably large.
strucchange: An R package for testing for structural change in linear regression models
 Journal of Statistical Software
"... This introduction to the R package strucchange is a (slightly) modified version of Zeileis, Leisch, Hornik, and Kleiber (2002), which reviews tests for structural change in linear regression models from the generalized fluctuation test framework as well as from the F test (Chow test) framework. Sinc ..."
Abstract

Cited by 22 (12 self)
 Add to MetaCart
This introduction to the R package strucchange is a (slightly) modified version of Zeileis, Leisch, Hornik, and Kleiber (2002), which reviews tests for structural change in linear regression models from the generalized fluctuation test framework as well as from the F test (Chow test) framework. Since Zeileis et al. (2002) various extensions were added to the package, in particular related to breakpoint estimation (also know as “dating”, discussed in Zeileis, Kleiber, Krämer, and Hornik 2003) and to structural change tests in other parametric models (Zeileis 2006). Here, we focus on the linear regression model and introduce a unified approach for implementing tests from the fluctuation test and F test framework for this model, illustrating how this approach has been realized in strucchange. Enhancing the standard significance test approach the package contains methods to fit, plot and test empirical fluctuation processes (like CUSUM, MOSUM and estimatesbased processes) and to compute, plot and test sequences of F statistics with the supF, aveF and expF test. Thus, it makes powerful tools available to display information about structural changes in regression relationships and to assess their significance. Furthermore, it is described how incoming data can be monitored.
Small Sample Properties of Forecasts from Autoregressive Models under Structural Breaks
 Journal of Econometrics
, 2005
"... This paper develops a theoretical framework for the analysis of smallsample properties of forecasts from general autoregressive models under structural breaks. Finitesample results for the mean squared forecast error of onestep ahead forecasts are derived, both conditionally and unconditionally, a ..."
Abstract

Cited by 18 (9 self)
 Add to MetaCart
This paper develops a theoretical framework for the analysis of smallsample properties of forecasts from general autoregressive models under structural breaks. Finitesample results for the mean squared forecast error of onestep ahead forecasts are derived, both conditionally and unconditionally, and numerical results for different types of break specifications are presented. It is established that forecast errors are unconditionally unbiased even in the presence of breaks in the autoregressive coefficients and/or error variances so long as the unconditional mean of the process remains unchanged. Insights from the theoretical analysis are demonstrated in Monte Carlo simulations and on a range of macroeconomic time series from G7 countries. The results are used to draw practical recommendations for the choice of estimation window when forecasting from autoregressive models subject to breaks. JEL Classifications: C22, C53.
Monitoring Structural Change
, 1996
"... This paper is organized as follows. In Section 2, we motivate and discuss the sequential testing approach. Section 3 discusses invariance principles of the past and present, and the CUSUM and fluctuation instability detectors. Section 4 contains some illustrative Monte Carlo experiments. A summary a ..."
Abstract

Cited by 16 (1 self)
 Add to MetaCart
This paper is organized as follows. In Section 2, we motivate and discuss the sequential testing approach. Section 3 discusses invariance principles of the past and present, and the CUSUM and fluctuation instability detectors. Section 4 contains some illustrative Monte Carlo experiments. A summary and concluding remarks are given in Section 5. Proofs are gathered into the Mathematical Appendix
Generalized MFluctuation Tests for Parameter Instability
"... A general class of fluctuation tests for parameter instability in an Mestimation framework is suggested. The tests are based on partial sum processes of Mestimation scores for which functional central limit theorems are derived under the null hypothesis of parameter stability and local alternative ..."
Abstract

Cited by 12 (11 self)
 Add to MetaCart
A general class of fluctuation tests for parameter instability in an Mestimation framework is suggested. The tests are based on partial sum processes of Mestimation scores for which functional central limit theorems are derived under the null hypothesis of parameter stability and local alternatives. Special emphasis is given to parameter instability in (generalized) linear regression models and it is shown that the introduced Mfluctuation tests contain a large number of parameter instability or structural change tests known from the statistics and econometrics literature. The usefulness of the procedures is illustrated using artificial data and data for the German M1 money demand, historical demographic time series from Groarl, Austria, and youth homicides in Boston.