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Seasonality Detection in Small Samples using Score-Driven Nonlinear Multivariate Dynamic Location Models

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dc.affiliation.dptoUC3M. Departamento de Economíaes
dc.contributor.authorBlazsek, Szabolcs
dc.contributor.authorEscribano, Álvaro
dc.contributor.authorLicht, Adrian
dc.contributor.editorUniversidad Carlos III de Madrid. Departamento de Economíaes
dc.date.accessioned2018-09-28T10:52:13Z
dc.date.available2018-09-28T10:52:13Z
dc.date.issued2018-09-12
dc.description.abstractWe suggest a new mechanism to detect stochastic seasonality of multivariate macroeconomic variables, by using an extension of the score-driven first-order multivariate t-distribution model. We name the new model as the quasi-vector autoregressive (QVAR) model. QVAR is a nonlinear extension of Gaussian VARMA (VAR moving average). The location of dependent variables for QVAR is updated by the score function, thus QVAR is robust to extreme observations. For QVAR, we present the econometric formulation, computation of the impulse response function (IRF), maximum likelihood (ML) estimation, and conditions of the asymptotic properties of ML that include invertibility. We use quarterly macroeconomic data for the period of 1987:Q1 to 2013:Q2 inclusive, which include extreme observations from three I(0) variables: percentage change in crude oil real price, United States (US) inflation rate, and US real gross domestic product (GDP) growth. The sample size of these data is relatively small, which occurs frequently in macroeconomic analyses. The statistical performance of QVAR is superior to that of VARMA and VAR. Annual seasonality effects are identified for QVAR, whereas those effects are not identified for VARMA and VAR. Our results suggest that QVAR may be used as a practical tool for seasonality detection in small macroeconomic datasets.en
dc.description.sponsorshipThe work was presented in “Recent Advances in Econometrics: International Conference in Honor of Luc Bauwens" (Brussels, 19-20 October 2017), GESG Research Seminar (Guatemala City, 9 November 2017), “Workshop in Time Series Econometrics" (Zaragoza, 12-13 April 2018), and “International Conference on Statistical Methods for Big Data" (Madrid, 7-8 July 2018). The authors are thankful to Luc Bauwens, Matthew Copley, Antoni Espasa, Eric Ghysels, Joachim Grammig, Andrew Harvey, S∅ren Johansen, Bent Nielsen, Eric Renault, Genaro Sucarrat and Ruey Tsay. Blazsek and Licht acknowledge funding from Universidad Francisco Marroquín. Escribano acknowledges funding from Ministerio de Economía, Industria y Competitividad (ECO2016-00105-001 and MDM 2014-0431) and Comunidad de Madrid (MadEco-CM S2015/HUM-3444).en
dc.format.mimetypeapplication/pdf
dc.identifier.issn2340-5031es
dc.identifier.urihttp://hdl.handle.net/10016/27483
dc.identifier.uxxiDT/0000001627es
dc.language.isoenges
dc.relation.ispartofseriesUC3M Working papers. Economicsen
dc.relation.ispartofseries18-09es
dc.relation.projectIDGobierno de España. ECO2016-00105-001es
dc.relation.projectIDGobierno de España. MDM 2014-0431es
dc.relation.projectIDComunidad de Madrid. S2015/HUM-3444es
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Españaes
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.jelC32es
dc.subject.jelC52es
dc.titleSeasonality Detection in Small Samples using Score-Driven Nonlinear Multivariate Dynamic Location Modelsen
dc.typeworking paper*
dc.type.hasVersionAO*
dspace.entity.typePublication
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