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dc.contributor.authorCasini, Alessandroen_US
dc.contributor.authorPerron, Pierreen_US
dc.date2018-06-27
dc.date.accessioned2018-02-06T02:40:25Z
dc.date.accessioned2019-07-18T12:31:18Z
dc.date.available2018-02-06T02:40:25Z
dc.date.available2019-07-18T12:31:18Z
dc.date.issued2019-12-06
dc.identifierhttps://oxfordre.com/economics/view/10.1093/acrefore/9780190625979.001.0001/acrefore-9780190625979-e-179
dc.identifier.citationCasini, A., & Perron, P. (2019, March 26). Structural Breaks in Time Series. Oxford Research Encyclopedia of Economics and Finance. https://doi.org/10.1093/acrefore/9780190625979.013.179
dc.identifier.urihttps://hdl.handle.net/2144/36593
dc.description.abstractThis article covers methodological issues related to estimation, testing, and computation for models involving structural changes. Our aim is to review developments as they relate to econometric applications based on linear models. Substantial advances have been made to cover models at a level of generality that allow a host of interesting practical applications. These include models with general stationary regressors and errors that can exhibit temporal dependence and heteroskedasticity, models with trending variables and possible unit roots and cointegrated models, among others. Advances have been made pertaining to computational aspects of constructing estimates, their limit distributions, tests for structural changes, and methods to determine the number of changes present. A variety of topics are covered including recent developments: testing for common breaks, models with endogenous regressors (emphasizing that simply using least-squares is preferable over instrumental variables methods), quantile regressions, methods based on Lasso, panel data models, testing for changes in forecast accuracy, factors models, and methods of inference based on a continuous records asymptotic framework. Our focus is on the so-called off-line methods whereby one wants to retrospectively test for breaks in a given sample of data and form confidence intervals about the break dates. The aim is to provide the readers with an overview of methods that are of direct use in practice as opposed to issues mostly of theoretical interest.en_US
dc.publisherOxford University Pressen_US
dc.relation.ispartofOxford Research Encyclopedia of Economics and Finance (forthcoming)
dc.relation.replaceshttps://hdl.handle.net/2144/26712
dc.relation.replaces2144/26712
dc.rightsReproduced by permission of Oxford University Press https://global.oup.com/academic/rights/permissions/autperm/?cc=gb&lang=en&. DOI: 10.1093/acrefore/9780190625979.013.179en_US
dc.subjectChange-pointen_US
dc.subjectLinear modelsen_US
dc.subjectTestingen_US
dc.subjectConfidence intervalsen_US
dc.subjectTrendsen_US
dc.subjectStationary and integrated regressorsen_US
dc.subjectFactor modelsen_US
dc.subjectLassoen_US
dc.subjectForecastsen_US
dc.titleStructural breaks in time seriesen_US
dc.typeBook chapteren_US
dc.description.versionAccepted manuscripten_US
dc.identifier.doi10.1093/acrefore/9780190625979.013.179
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-statusPublisheden_US
dc.identifier.mycv297737


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