dc.contributor | Calderón Villanueva, Sergio Alejandro | |
dc.contributor | Series de Tiempo | |
dc.creator | Ibáñez Forero, Luis Eduardo | |
dc.date.accessioned | 2020-07-17T15:58:31Z | |
dc.date.available | 2020-07-17T15:58:31Z | |
dc.date.created | 2020-07-17T15:58:31Z | |
dc.date.issued | 2020-05-07 | |
dc.identifier | https://repositorio.unal.edu.co/handle/unal/77786 | |
dc.description.abstract | Sometimes it is necessary to work with multivariate time series that have heavy tails and in particular multivariate Student t-noise. Unfortunately, there is no Bayesian methodology in the literature known to the author that allows estimating the structural parameters of a multivariate TAR model. In this sense, the analysis of the autoregressive multivariate models of thresholds and multivariate t-Student noise is carried out via the Bayesian approach and the proposed methodology is examined through simulations and an application in the stock market field. | |
dc.description.abstract | En algunas ocasiones es necesario trabajar con series de tiempo multivariadas que tienen colas pesadas y en particular ruido t-Student multivariado. Desafortunadamente no existe en la literatura, conocida por el autor, alguna metodología Bayesiana que permita estimar los parámetros estructurales de un modelo TAR multivariado. En ese sentido el análisis de los modelos multivariados autoregresivos de umbrales y ruido t-Student multivariado es llevado a cabo vía el enfoque Bayesiano y la metodología propuesta es examinada a través de simulaciones y una aplicación en el campo bursátil. | |
dc.language | spa | |
dc.publisher | Bogotá - Ciencias - Maestría en Ciencias - Estadística | |
dc.publisher | Departamento de Estadística | |
dc.publisher | Universidad Nacional de Colombia - Sede Bogotá | |
dc.relation | Calderón, S. & Nieto, F. (2017), ‘Bayesian analysis of multivariate threshold autoregressive
models with missing data’, Communications in Statistics - Theory and Methods
46(1), 296–318. | |
dc.relation | Carlin, B. & Chib, S. (1995), ‘Bayesian model choice via markov chain monte carlo methods’,
Journal of the Royal Statistical Society. Series B (Methodological) 57(13), 473–484. | |
dc.relation | Chen, C. (1999), ‘Subset selection of autoregressive time series models’, Journal of Forecasting
18(7), 505–516. | |
dc.relation | Chen, C., Liu, F.-C. & So, M. (2011), ‘A review of threshold time series models in finance’,
Statistics and its Interface 4(2), 167–181. | |
dc.relation | Chen, C. & So, M. (2003), ‘Subset threshold autoregression’, Journal of Forecasting 22, 49–
66. | |
dc.relation | Dellaportas, P., Forster, J. & Ntzoufras, I. (2002), ‘On bayesian model and variable selection
using mcmc’, Statistics and Computing 12(1), 27–36. | |
dc.relation | Gelman, A. & Rubin, D. (1992), ‘Inference from iterative simulation using multiple sequences’,
Statistical Science 7(4), 457–472. | |
dc.relation | Gelman, A., Stern, H., Carlin, J., Dunson, D., Vehtari, A. & Rubin, D. (2013), Bayesian
data analysis, Chapman and Hall/CRC. | |
dc.relation | Geweke, J. (1992), ‘Evaluating the accuracy of sampling-based approaches to the calculation
of posterior moments’, Bayesian Statistics, Clarendon Press, Oxford. 4, 169–193. | |
dc.relation | Green, P. (1995), ‘Reversible jump markov chain monte carlo computation and bayesian
model determination’, Biometrika 82(4), 711–732. | |
dc.relation | Hansen, B. (1999), ‘Testing for linearity’, Journal of Economic Surveys 13(5), 551–76. | |
dc.relation | Hansen, B. (2011), ‘Threshold autoregression in economics’, Statistics and its Interface
4(2), 123–127. | |
dc.relation | Kotz, S. & Nadarajah, S. (2004), Multivariate T-Distributions and Their Applications, Cambridge
University Press. | |
dc.relation | Kuo, L. & Mallick, B. (1998), ‘Variable selection for regression models’, The Indian Journal
of Statistics, Series B 60(1), 65–81. | |
dc.relation | Lin, P.-E. (1972), ‘Some characterizations of the multivariate t distribution’, Journal of
Multivariate Analysis 2(3), 339–344. | |
dc.relation | Liu, J. (1996), ‘Peskun’s theorem and a modified discrete-state gibbs sampler’, Biometrika
83(3), 681–682. | |
dc.relation | Lo, M. & Zivot, E. (2001), ‘Threshold cointegration and nonlinear adjustment to the law
of one price’, Macroeconomic Dynamics 5(4), 533–576. | |
dc.relation | Millan, C. & Calderón, S. (2017), Estimación Bayesiana de los parámetros de umbrales
y de retardo en un modelo autorregresivo de umbrales multivariado (mtar) con ruido
t-student multivariado, Master’s thesis. | |
dc.relation | Nieto, F. (2005), ‘Modeling bivariate threshold autoregressive processes in the presence
of missing data’, Communications in Statistics - Theory and Methods 34(4), 905–930. | |
dc.relation | Robert, C. & Casella, G. (2005), Monte Carlo Statistical Methods (Springer Texts in Statistics),
Springer-Verlag New York, Inc. | |
dc.relation | Robert, C. & Casella, G. (2009), Introducing Monte Carlo Methods with R (Use R), 1st edn,
Springer-Verlag. | |
dc.relation | Romero, L. (2017), Estimación Bayesiana de un modelo tar multivariado cuando el proceso
de ruido sigue una distribución t-student, Master’s thesis. | |
dc.relation | Schäfer, C. (2012), Monte Carlo methods for sampling high-dimensional binary vectors,
PhD thesis, Universit´e Paris Dauphine-Paris IX. | |
dc.relation | Schäfer, C. & Chopin, N. (2011), ‘Sequential monte carlo on large binary sampling spaces’,
Statistics and Computing 23, 163–184. | |
dc.relation | Stenseth, N., Chan, K., Tong, H., Boonstra, R., Boutin, S., Krebs, C., Post, E., O’Donoghue,
M., Yoccoz, N., Forchhammer, M. & Hurrell, J. (1999), ‘Common dynamic structure of
canada lynx populations within three climatic regions’, Science 285(5430), 1071–1073. | |
dc.relation | Tong, H. (1978), ‘On a threshold model’, In Pattern Recognition and Signal Processing (C.
H. Chen, ed.) 29, 575–586. | |
dc.relation | Tong, H. (1983), Threshold models in non-linear time series analysis, Springer-Verlag. | |
dc.relation | Tsay, R. (1998), ‘Testing and modelingmultivariate threshold models’, Journal of the American
Statistical Association 93(443), 1188–1202. | |
dc.relation | Zhang, H. & Nieto, F. H. (2015), ‘Tar modeling with missing data when the white noise
process follows a student’s t-distribution’, Revista Colombiana de Estad´ıstica 38(1), 239–
266. | |
dc.rights | Atribución-SinDerivadas 4.0 Internacional | |
dc.rights | Acceso abierto | |
dc.rights | http://creativecommons.org/licenses/by-nd/4.0/ | |
dc.rights | info:eu-repo/semantics/openAccess | |
dc.rights | Derechos reservados - Universidad Nacional de Colombia | |
dc.title | Estimación Bayesiana de los parámetros estructurales de los modelos Multivariados Autoregresivos de Umbrales con ruido t-Student multivariado | |
dc.type | Otro | |