Testing Jointly for Structural Changes in the Error Variance and Coefficients of a Linear Regression Model

Pierre Perron and Jing Zhou ()
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Jing Zhou: BlackRock, Inc.

No wp2008-011, Boston University - Department of Economics - Working Papers Series from Boston University - Department of Economics

Abstract: We provide a comprehensive treatment of the problem of testing jointly for structural change in both the regression coefficients and the variance of the errors in a single equation regression involving stationary regressors. Our framework is quite general in that we allow for general mixing-type regressors and the assumptions imposed on the errors are quite mild. The errors’ distribution can be non-normal and conditional heteroskedasticity is permissable. Extensions to the case with serially correlated errors are also treated. We provide the required tools for addressing the following testing problems, among others: a) testing for given numbers of changes in regression coefficients and variance of the errors; b) testing for some unknown number of changes less than some pre-specified maximum; c) testing for changes in variance (regression coefficients) allowing for a given number of changes in regression coefficients (variance); and d) estimating the number of changes present. These testing problems are important for practical applications as witnessed by recent interests in macroeconomics and finance for which documenting structural change in the variability of shocks to simple autoregressions or vector autoregressive models has been a concern.

Keywords: Change-point; Variance shift; Conditional heteroskedasticity; Likelihood ratio tests (search for similar items in EconPapers)
Pages: 35
Date: 2008-07
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (9)

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Related works:
Journal Article: Testing jointly for structural changes in the error variance and coefficients of a linear regression model (2020)
Working Paper: Testing Jointly for Structural Changes in the Error Variance and Coefficients of a Linear Regression Model (2019)
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