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dc.contributor.authorXu, Jiawenen_US
dc.contributor.authorPerron, Pierreen_US
dc.date.accessioned2018-02-06T03:15:25Z
dc.date.accessioned2019-12-19T13:57:27Z
dc.date.available2018-02-06T03:15:25Z
dc.date.available2019-12-19T13:57:27Z
dc.date.issued2018
dc.identifier.citationJiawen Xu, Pierre Perron. 2018. "Forecasting in the Presence of In and Out of Sample Breaks."
dc.identifier.issn0735-0015
dc.identifier.urihttps://hdl.handle.net/2144/39010
dc.description.abstractWe present a frequentist-based approach to forecast time series in the presence of in-sample and out-of-sample breaks in the parameters of the forecasting model. We first model the parameters as following a random level shift process, with the occurrence of a shift governed by a Bernoulli process. In order to have a structure so that changes in the parameters be forecastable, we introduce two modifications. The first models the probability of shifts according to some covariates that can be forecasted. The second incorporates a built-in mean reversion mechanism to the time path of the parameters. Similar modifications can also be made to model changes in the variance of the error process. Our full model can be cast into a conditional linear and Gaussian state space framework. To estimate it, we use the mixture Kalman filter and a Monte Carlo expectation maximization algorithm. Simulation results show that our proposed forecasting model provides improved forecasts over standard forecasting models that are robust to model misspecifications. We provide two empirical applications and compare the forecasting performance of our approach with a variety of alternative methods. These show that substantial gains in forecasting accuracy are obtained.en_US
dc.relation.replaceshttps://hdl.handle.net/2144/26715
dc.relation.replaces2144/26715
dc.subjectInstabilitiesen_US
dc.subjectStructural changeen_US
dc.subjectForecastingen_US
dc.subjectRandom level shiftsen_US
dc.subjectMixture Kalman filteren_US
dc.subjectEconometricsen_US
dc.subjectMathematical sciencesen_US
dc.subjectEconomicsen_US
dc.subjectCommerce, management, tourism and servicesen_US
dc.titleForecasting in the presence of in and out of sample breaksen_US
dc.typeArticleen_US
dc.description.versionFirst author draften_US
pubs.elements-sourcemanual-entryen_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Economicsen_US
pubs.publication-statusUnpublisheden_US
dc.identifier.mycv297742


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