Univariate conditional distributions of an Open-Loop TAR stochastic process

dc.contributor.authorNieto, Fabio H.spa
dc.contributor.authorMoreno, Edna C.spa
dc.coverage.campusCRAI-USTA Bogotáspa
dc.date.accessioned2019-12-04T14:21:55Zspa
dc.date.available2019-12-04T14:21:55Zspa
dc.date.issued2016-07-01spa
dc.descriptionEn trayectorias de un proceso estocástico autoregresivo de umbrales (TAR), sin retroalimentación, se observan conglomerados de valores extremos. Con el fin de caracterizar el mecanismo probabilístico que los genera, en este artículo se estudian tres tipos de distribuciones marginales condicionales del proceso subyacente. Uno de ellos permite encontrar la función de varianza condicional que explica ese hecho estilizado del proceso. Como un resultado adicional, se obtiene una condición suficiente para determinar estacionariedad débil asintótica, de un proceso TAR sin retroalimentación.spa
dc.description.abstractClusters of large values are observed in sample paths of certain open-loop threshold autoregressive (TAR) stochastic processes. In order to characterize the stochastic mechanism that generates this empirical stylized fact, three types of marginal conditional distributions of the underlying stochastic process are analyzed in this paper. One allows us to find the conditional variance function that explains the aforementioned stylized fact. As a by-product, we are able to derive a sufficient condition to have asymptotic weak stationarity in an open-loop TAR stochastic process.spa
dc.description.domainhttp://unidadinvestigacion.usta.edu.cospa
dc.format.mimetypeapplication/pdfspa
dc.identifier.doihttps://doi.org/10.15446/rce.v39n2.58912spa
dc.identifier.urihttp://hdl.handle.net/11634/20116
dc.relation.referencesBillingsley, P. (1995), Probability and Measure, John Wiley & Sons, Inc., New Jersey.spa
dc.relation.referencesBollerslev, T. (1986), ‘Generalized autoregressive conditional heteroskedasticity’, Journal of Econometrics 31, 307–327.spa
dc.relation.referencesBrockwell, P. & Davis, R. (1991), Time Series: Theory and Methods, Springer- Verlag, New York.spa
dc.relation.referencesChen, R. & Tsay, R. (1991), ‘On the ergodicity of TAR(1) processes ’, The Annals of Applied Probability 1, 613–634.spa
dc.relation.referencesEngle, R. (1982), ‘Autoregressive conditional heteroscedasticity with estimates of the variance of the United Kingdom inflation’, Econometrica 50, 987–1006.spa
dc.relation.referencesHoyos, N. (2006), Una aplicación del modelo no lineal tar en economía, Tesis de Maestría, Universidad Nacional de Colombia, Facultad de Ciencias. Departamento de Estadística, Bogotá.spa
dc.relation.referencesLi, D., Ling, S. & Tong, H. (2012), ‘On moving-average models with feedback’, Bernoulli 18, 735–745.spa
dc.relation.referencesMoreno, E. (2011), Una aplicación del modelo tar en series de tiempo financieras, Tesis de Maestría, Universidad Nacional de Colombia, Facultad de Ciencias. Departamento de Estadística, Bogotá.spa
dc.relation.referencesMoreno, E. & Nieto, F. (2014), ‘Modelos TAR en series de tiempo financieras’, Comunicaciones en Estadística 7, 223–243.spa
dc.relation.referencesMorettin, P. (2008), Econometria Financeira-Um Curso em Séries Temporais Financeiras, Editora Blucher, Brasil.spa
dc.relation.referencesNieto, F. (2005), ‘Modeling Bivariate Threshold Autoregressive Processes in the Presence of Missing Data’, Communications in Statistics: Theory and Methods 34, 905–930.spa
dc.relation.referencesNieto, F. (2008), ‘Forecasting with univariate TAR models’, Statistical Methodology 5, 263–276.spa
dc.relation.referencesNieto, F., Zhang, H. & Li, W. (2013), ‘Using the Reversible Jump MCMC procedure for identifying and estimating univariate TAR models’, Communications in Statistics: Simulation and Computation 42, 814–840.spa
dc.relation.referencesPetruccelli, J. & Woolford, S. (1984), ‘A Threshold AR(1) Model’, Journal of Applied Probability 21, 270–286.spa
dc.relation.referencesShao, J. (2003), Mathematical Statistics, Springer-Verlag, New York.spa
dc.relation.referencesTong, H. (1978), On a threshold model, in C. H. Chen, ed., ‘Pattern Recognition and Signal Processing’, Amsterdam, pp. 575–586.spa
dc.relation.referencesTong, H. (1990), Nonlinear Time Series, Oxford University Press, Oxford.spa
dc.relation.referencesTong, H. (2011), ‘Threshold models in time series analysis-30 years on’, Statistics and Its Interface 28, 107–118.spa
dc.relation.referencesTsay, R. (1998), ‘Testing and modeling multivariate threshold models’, Journal of the American Statistical Association 93, 1188–1202.spa
dc.relation.referencesWong, C. & Li, W. (2000), ‘On a mixture autoregressive model’, Journal of the Royal Statistical Society B 62, 95–115.spa
dc.rightsAtribución-NoComercial-CompartirIgual 2.5 Colombia*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/2.5/co/*
dc.subject.keywordConditional heteroscedasticityspa
dc.subject.keywordNonlinear stochastic processspa
dc.subject.keywordOpen-loop TAR modelspa
dc.subject.keywordStationary nonlinear stochastic processspa
dc.subject.proposalHeterocedasticidad condicionalspa
dc.subject.proposalModelo TAR sin retroalimentaciónspa
dc.subject.proposalProceso estocástico no lineal estacionariospa
dc.titleUnivariate conditional distributions of an Open-Loop TAR stochastic processspa
dc.type.categoryGeneración de Nuevo Conocimiento: Artículos publicados en revistas especializadas - Electrónicosspa

Archivos

Bloque original

Mostrando 1 - 1 de 1
Cargando...
Miniatura
Nombre:
Univariate conditional distributions of an Open-Loop TAR stochastic process.pdf
Tamaño:
1.02 MB
Formato:
Adobe Portable Document Format
Descripción:
Artículo SCOPUS

Bloque de licencias

Mostrando 1 - 1 de 1
Thumbnail USTA
Nombre:
license.txt
Tamaño:
807 B
Formato:
Item-specific license agreed upon to submission
Descripción: