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Timing structural change: a conditional probabilistic approach
Author(s) -
DeJong David N.,
Liesenfeld Roman,
Richard JeanFrancois
Publication year - 2006
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.821
Subject(s) - probabilistic logic , econometrics , parametric statistics , stability (learning theory) , computer science , variance (accounting) , monte carlo method , volatility (finance) , conditional expectation , statistics , economics , mathematics , machine learning , artificial intelligence , accounting
We propose a strategy for assessing structural stability in time‐series frameworks when potential change dates are unknown. Existing stability tests are effective in detecting structural change, but procedures for identifying timing are imprecise, especially in assessing the stability of variance parameters. We present a likelihood‐based procedure for assigning conditional probabilities to the occurrence of structural breaks at alternative dates. The procedure is effective in improving the precision with which inferences regarding timing can be made. We illustrate parametric and non‐parametric implementations of the procedure through Monte Carlo experiments, and an assessment of the volatility reduction in the growth rate of US GDP. Copyright © 2006 John Wiley & Sons, Ltd.