Results 1  10
of
70
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 252 (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...
Sample Splitting and Threshold Estimation
 Econometrica
, 2000
"... Threshold models have a wide variety of applications in economics. Direct applications include models of separating and multiple equilibria. Other applications include empirical sample splitting when the sample split is based on a continuouslydistributed variable such as firm size. In addition, thr ..."
Abstract

Cited by 82 (3 self)
 Add to MetaCart
Threshold models have a wide variety of applications in economics. Direct applications include models of separating and multiple equilibria. Other applications include empirical sample splitting when the sample split is based on a continuouslydistributed variable such as firm size. In addition, threshold models may be used as a parsimonious strategy for nonparametric function estimation. For example, the threshold autoregressive model Ž TAR. is popular in the nonlinear time series literature. Threshold models also emerge as special cases of more complex statistical frameworks, such as mixture models, switching models, Markov switching models, and smooth transition threshold models. It may be important to understand the statistical properties of threshold models as a preliminary step in the development of statistical tools to handle these more complicated structures. Despite the large number of potential applications, the statistical theory of threshold estimation is undeveloped. It is known that threshold estimates are superconsistent, but a distribution theory useful for testing and inference has yet to be provided. This paper develops a statistical theory for threshold estimation in the regression context. We allow for either crosssection or time series observations. Least squares estimation of the regression parameters is considered. An asymptotic distribution theory for the regression estimates Ž the threshold and the regression slopes. is developed. It is found that the distribution of the threshold estimate is nonstandard. A method to construct asymptotic confidence intervals is developed by inverting the likelihood ratio statistic. It is shown that this yields asymptotically conservative confidence regions. Monte Carlo simulations are presented to assess the accuracy of the asymptotic approximations. The empirical relevance of the theory is illustrated through an application to the multiple equilibria growth model of Durlauf and Johnson Ž 1995..
Understanding Trend and Cycle in Asset Values: Reevaluating the Wealth Effect on Consumption
 American Economic Review
, 2004
"... Both textbook economics and common sense teach us that the value of household wealth should be related to consumer spending. Early academic work by Franco Modigliani (1971) suggested that a dollar increase in wealth (holding � xed labor income) leads to an increase in consumer spending of about � ve ..."
Abstract

Cited by 60 (4 self)
 Add to MetaCart
Both textbook economics and common sense teach us that the value of household wealth should be related to consumer spending. Early academic work by Franco Modigliani (1971) suggested that a dollar increase in wealth (holding � xed labor income) leads to an increase in consumer spending of about � ve cents. Since then, the socalled “wealth effect ” on consumption has increasingly crept into both mainstream and policy discussions of the macroeconomy. 1 Today, it is commonly presumed that signi �cant movements in wealth will be associated with movements in consumer spending, either contemporaneously or subsequently. Quantitative estimates of roughly the magnitude reported by Modigliani are routinely cited in
Testing for Linearity
 Journal of Economic Surveys
, 1999
"... Abstract. The problem of testing for linearity and the number of regimes in the context of selfexciting threshold autoregressive (SETAR) models is reviewed. We describe leastsquares methods of estimation and inference. The primary complication is that the testing problem is nonstandard, due to th ..."
Abstract

Cited by 35 (1 self)
 Add to MetaCart
Abstract. The problem of testing for linearity and the number of regimes in the context of selfexciting threshold autoregressive (SETAR) models is reviewed. We describe leastsquares methods of estimation and inference. The primary complication is that the testing problem is nonstandard, due to the presence of parameters which are only defined under the alternative, so the asymptotic distribution of the test statistics is nonstandard. Simulation methods to calculate asymptotic and bootstrap distributions are presented. As the sampling distributions are quite sensitive to conditional heteroskedasticity in the error, careful modeling of the conditional variance is necessary for accurate inference on the conditional mean. We illustrate these methods with two applications Ð annual sunspot means and monthly U.S. industrial production. We find that annual sunspots and monthly industrial production are SETAR(2) processes. Keywords. SETAR models; Thresholds; Nonstandard asymptotic theory; Bootstrap
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 32 (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.
Stochastic Permanent Breaks
 Review of Economics and Statistics
, 1998
"... This paper aims to bridge the gap between processes where shocks are permanent and those with transitory shocks by formulating a process in which the long run impact of each innovation is time varying and stochastic. Frequent transitory shocks are supplemented by occasional permanent shifts. The sto ..."
Abstract

Cited by 31 (0 self)
 Add to MetaCart
This paper aims to bridge the gap between processes where shocks are permanent and those with transitory shocks by formulating a process in which the long run impact of each innovation is time varying and stochastic. Frequent transitory shocks are supplemented by occasional permanent shifts. The stochastic permanent breaks (STOPBREAK) process is based on the premise that a shock is more likely to be permanent if it is large than if it is small. This formulation is motivated by a class of processes that undergo random structural breaks. Consistency and asymptotic normality of quasi maximum likelihood estimates is established and locally best hypothesis tests of the null of a random walk are developed. The model is applied to relative prices of pairs of stocks and significant test statistics result. KEYWORDS: Structural breaks, nonlinear moving average, unit roots, quasi maximum likelihood estimation, NeymanPearson testing, locally best test, temporary cointegration. 1. INTRODUCTION Time series analysts tend to draw a sharp line between processes where shocks have a permanent effect and those where they do not. The most notable example of this is the distinction between stationary AR(1) processes, where all shocks are transitory, and the random walk. As the autoregressive root approaches one, the rate at which shocks are expected to decay decreases, but they remain transitory. This paper aims to bridge the gap between transience and permanence by formulating a process in which the long run impact of each observation is time varying and stochastic. At one extreme all innovations are transitory and at the other, all shocks are permanent. 2
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 25 (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.