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2011-08-03Buch DOI: 10.18452/4336
Large Vector Auto Regressions
dc.contributor.authorSong, Song
dc.contributor.authorBickel, Peter J.
dc.date.accessioned2017-06-16T00:25:29Z
dc.date.available2017-06-16T00:25:29Z
dc.date.created2011-08-09
dc.date.issued2011-08-03
dc.date.submitted2011-08-03
dc.identifier.issn1860-5664
dc.identifier.urihttp://edoc.hu-berlin.de/18452/4988
dc.description.abstractOne popular approach for nonstructural economic and financial forecasting is to include a large number of economic and financial variables, which has been shown to lead to significant improvements for forecasting, for example, by the dynamic factor models. A challenging issue is to determine which variables and (their) lags are relevant, especially when there is a mixture of serial correlation (temporal dynamics), high dimensional (spatial) dependence structure and moderate sample size (relative to dimensionality and lags). To this end, an integrated solution that addresses these three challenges simultaneously is appealing. We study the large vector auto regressions here with three types of estimates. We treat each variable's own lags different from other variables' lags, distinguish various lags over time, and is able to select the variables and lags simultaneously. We first show the consequences of using Lasso type estimate directly for time series without considering the temporal dependence. In contrast, our proposed method can still produce an estimate as efficient as an oracle under such scenarios. The tuning parameters are chosen via a data driven "rolling scheme" method to optimize the forecasting performance. A macroeconomic and financial forecasting problem is considered to illustrate its superiority over existing estimators.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectTime Serieseng
dc.subjectRegularizationeng
dc.subjectGroup Lassoeng
dc.subjectVector Auto Regressioneng
dc.subjectLassoeng
dc.subjectOracle estimatoreng
dc.subject.ddc330 Wirtschaft
dc.titleLarge Vector Auto Regressions
dc.typebook
dc.identifier.urnurn:nbn:de:kobv:11-100190520
dc.identifier.doihttp://dx.doi.org/10.18452/4336
local.edoc.container-titleSonderforschungsbereich 649: Ökonomisches Risiko
local.edoc.pages32
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-volume2011
local.edoc.container-issue48
local.edoc.container-year2011
local.edoc.container-erstkatid2195055-6

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