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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 ..."
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Cited by 255 (13 self)
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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...
Testing for a signal with unknown location and scale in a stationary Gaussian random field
, 1995
"... this paper are concerned with approximate evaluation of the significance level of the test defined by (1.5), i.e., the probability when = 0 that X max exceeds a constant threshold, say b. First order approximations for this can easily be derived from the results going back to Belyaev and Pitaberg ( ..."
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Cited by 52 (18 self)
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this paper are concerned with approximate evaluation of the significance level of the test defined by (1.5), i.e., the probability when = 0 that X max exceeds a constant threshold, say b. First order approximations for this can easily be derived from the results going back to Belyaev and Pitaberg (1972) (see Adler, 1981, Theorem 6.9.1, p. 160) who give the the following. Suppose Y (r) is a zero mean, unit variance, stationary random field defined on an interval S ae IR
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 ..."
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Cited by 25 (7 self)
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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.
Kernel changepoint analysis
 in "Proc. Neural Info. Proc. Systems
, 2008
"... We introduce a kernelbased method for changepoint analysis within a sequence of temporal observations. Changepoint analysis of an unlabelled sample of observations consists in, first, testing whether a change in the distribution occurs within the sample, and second, if a change occurs, estimating ..."
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Cited by 17 (1 self)
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We introduce a kernelbased method for changepoint analysis within a sequence of temporal observations. Changepoint analysis of an unlabelled sample of observations consists in, first, testing whether a change in the distribution occurs within the sample, and second, if a change occurs, estimating the changepoint instant after which the distribution of the observations switches from one distribution to another different distribution. We propose a test statistic based upon the maximum kernel Fisher discriminant ratio as a measure of homogeneity between segments. We derive its limiting distribution under the null hypothesis (no change occurs), and establish the consistency under the alternative hypothesis (a change occurs). This allows to build a statistical hypothesis testing procedure for testing the presence of a changepoint, with a prescribed falsealarm probability and detection probability tending to one in the largesample setting. If a change actually occurs, the test statistic also yields an estimator of the changepoint location. Promising experimental results in temporal segmentation of mental tasks from BCI data and pop song indexation are presented. 1
Critical Values and P Values of Bessel Process Distributions: Computation and Application to Structural Break Tests
"... Schich, participants in a workshop at the Federal Reserve Bank of New York, and the referees. The views expressed in this paper are those of the author and do not necessarily represent those of the Federal Reserve Bank of New York or the Federal Reserve System. ..."
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Cited by 4 (0 self)
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Schich, participants in a workshop at the Federal Reserve Bank of New York, and the referees. The views expressed in this paper are those of the author and do not necessarily represent those of the Federal Reserve Bank of New York or the Federal Reserve System.