Estimating restricted structural change models

By Pierre Perron, Zhongjun Qu. 2006

156 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/17047aa40800fd4bdf24911a021a573e5ba574a1

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In Economics and Econometrics

Citations in the corpus

Selective review of offline change point detection methods (2020, methodology): “The most general of those sets can be found in [79].”
Threshold Regression with Endogeneity (2017, unknown): “In their work, PY (2015) assume the projection on x takes the form x′φ11(q ≤ γ ) + x′φ21(q > γ ) and their derivations apply earlier results from Perron and Qu (2006) which essentially require that the conditional expectation E [ε|x] = 0, whereas under endogeneity the conditional expectation E…”
GLS-based unit root tests with multiple structural breaks both under the null and the alternative hypotheses (2007, unknown): “With this specification, the estimation of the break dates can be done using an iterative procedure similar to that of Perron and Qu (2006).”
Estimating and Testing Structural Changes in Multivariate Regressions (2007, unknown): “(1997): “L’Estimation de Modèles avec Changements Structurels Multiples,” Actualité Économique, 73, 457-505 [28] Perron, P.”
Dealing with Structural Breaks (2005, methodology): “For models with restrictions (including the partial structural change model), an iterative procedure is available, which in most cases requires very few iterations (see Bai and Perron, 2003, and Perron and Qu, 2005, who make available Gauss codes to perform these and other tasks).”
STRUCTURAL THRESHOLD REGRESSION (2016, unknown): “Our finding is similar to the result of Perron and Qu (2006) who consider change-point models with restrictions across regimes.1 An additional implication of having different inverse Mills ratio terms across regimes is that the errors of the STR model are regime-specific heteroskedastic.”
A review of change point detection methods (2018, background): “multiple changes [74]. All of those results are obtained under various sets of general assumptions on the distributions of the covariates and the noise. The most general of those sets can be found in [79]. Roughly, in addition to some technical assumptions, it imposes the processes xand zto be weakl”
GLS-BASED UNIT ROOT TESTS WITH MULTIPLE STRUCTURAL BREAKS UNDER BOTH THE NULL AND THE ALTERNATIVE HYPOTHESES (2009, methodology): “We have also extended the analysis to the multiple break case made possible by a modification of the dynamic programming algorithm as described in Bai and Perron (2003) and Perron and Qu (2006).”