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

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dc.contributor.author Blazsek, Szabolcs
dc.contributor.author Escribano, Álvaro
dc.contributor.author Licht, Adrian
dc.contributor.editor Universidad Carlos III de Madrid. Departamento de Economía
dc.date.accessioned 2018-09-28T10:52:13Z
dc.date.available 2018-09-28T10:52:13Z
dc.date.issued 2018-09-12
dc.identifier.issn 2340-5031
dc.identifier.uri http://hdl.handle.net/10016/27483
dc.description.abstract We 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.
dc.description.sponsorship The 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).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.relation.ispartofseries UC3M Working papers. Economics
dc.relation.ispartofseries 18-09
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.title Seasonality Detection in Small Samples using Score-Driven Nonlinear Multivariate Dynamic Location Models
dc.type workingPaper
dc.subject.jel C32
dc.subject.jel C52
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. ECO2016-00105-001
dc.relation.projectID Gobierno de España. MDM 2014-0431
dc.relation.projectID Comunidad de Madrid. S2015/HUM-3444
dc.type.version draft
dc.identifier.uxxi DT/0000001627
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