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Structural instability and predictability
journal contribution
posted on 2019-11-01, 00:00 authored by N Devpura, Paresh Narayan, Susan SharmaSusan SharmaWe propose a structural break predictive regression model that accounts for predictor persistency, endogeneity, heteroscedasticity, and a structural break. Monte Carlo (MC) simulations indicate that this test performs satisfactorily compared to competitor estimators. We employ a popular U.S. data set (the period January 1927 to December 2016) that includes stock market returns and multiple predictors. We show, consistent with the MC results, evidence of a structural break. Our analysis reveals that a structural break–based predictive regression model fits the data reasonably well in predicting stock price returns.
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Journal
Journal of international financial markets, institutions and moneyVolume
63Article number
101145Pagination
1 - 13Publisher
ElsevierLocation
Amsterdam, The NetherlandsPublisher DOI
ISSN
1042-4431Language
engPublication classification
C1 Refereed article in a scholarly journal; C Journal articleUsage metrics
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