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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Azam, Kazim; Pitt, Michael K. (2014)
Publisher: University of Warwick. Department of Economics
Languages: English
Types: Book
Subjects: QA
This paper presents a method to specify a strictly stationary univariate time series model with particular emphasis on the marginal characteristics (fat tailedness, skewness etc.). It is the first time in time series models with specified marginal distribution, a non-parametric specification is used. Through a Copula distribution, the\ud marginal aspect are separated and the information contained within the order statistics allow to efficiently model a discretely-varied time series. The estimation is done through Bayesian method. The method is invariant to any copula family and for any level of heterogeneity in the random variable. Using count times series of weekly rearm homicides in Cape Town, South Africa, we show our method efficiently estimates the copula parameter representing the first-order Markov chain transition density.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Al-Osh, M. A. and Alzaid, A. A. First-order integer-valued autoregressive (inar(1)) process. Journal of Time Series Analysis, 8(3):261{275, 1987.
    • Baillie, Richard T.; Bollerslev, Tim, and Mikkelsen, Hans Ole. Fractionally integrated generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 74(1):3{30, September 1996.
    • Beare, Brendan K. Copulas and temporal dependence. Econometrica, 78(1):395{410, 2010a.
    • Beare, Brendan K. Archimedean copulas and temporal dependence. University of california at san diego, economics working paper series, Department of Economics, UC San Diego, Sep 2010b.
    • Bollerslev, Tim. Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3): 307{327, April 1986.
    • Bouye, Eric; Durrleman, Valdo; Nikeghbali, Ashkan; Riboulet, Gael, and Roncalli, Thierry. Copulas for nance - a reading guide and some applications. Social Science Research Network Working Paper Series, November 2007.
    • Chen, Xiaohong and Fan, Yanqin. Estimation of copula-based semiparametric time series models. Working Papers 0226, Department of Economics, Vanderbilt University, October 2002.
    • Chen, Xiaohong; Wu, Wei Biao, and Yi, Yanping. E cient estimation of copula-based semiparametric markov models. Cowles Foundation Discussion Papers 1691, Cowles Foundation for Research in Economics, Yale University, February 2009.
    • Cherubini, U. and Luciano, E. Value-at-risk trade-o and capital allocation with copulas. Economic Notes, 30 (2), 2001.
    • Chib, Siddhartha and Greenberg, Edward. Analysis of multivariate probit models. Biometrika, pages 347{361, 1998.
    • Chib, Siddhartha and Winkelmann, Rainer. Markov chain monte carlo analysis of correlated count data. Journal of Business & Economic Statistics, 19(4):428{35, 2001.
    • W.Darswo, B. Nguyen and Olsen, E. Copulas and markov processes. Illinois Journal of Mathematics, 36: 600{642, 1992.
    • Demarta, Stefano and McNeil, Alexander J. The t copula and related copulas. International Statistical Review, 73:111{129, 2005.
    • Embrechts, P.; Lindskog, F., and Mcneil, A. Modelling Dependence with Copulas and Applications to Risk Management. In Handbook of Heavy Tailed Distributions in Finance, chapter 8, pages 329{384. 2003.
    • Embrechts, Paul; McNeil, Alexander, and Straumann, Daniel. Correlation and dependence in risk management: Properties and pitfalls. In Risk Management: Value At Risk And Beyond, pages 176{223. Cambridge University Press, 1999.
    • Engle, Robert F. Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom in ation. Econometrica, 50(4):987{1007, July 1982.
    • Freeland, R.K. Statistical analysis of discrete time series with applications to the anaylsis of worker's compensation claims data. PhD thesis, The University of British Columbia, Canada, 1998.
    • Freeland, R.K. and McCabe, B.P.M. Forecasting discrete valued low count time series. International Journal of Forecasting, 20(3):427 { 434, 2004.
    • Genest, C. and Neslehova, J. A primer on copulas for count data. Astin Bulletin, 37(2):475, 2007.
    • Ho , Peter D. Extending the rank likelihood for semiparametric copula estimation. Ann. Appl. Stat., 1(1): 265{283, 2007.
    • Ibragimov, Rustam. Copula-based characterizations for higher order markov processes. Econometric Theory, 25(03):819{846, June 2009.
    • Joe, H. Multivariate Models and Dependence Concepts. Chapman & Hall/CRC, 1997.
    • Lentzas, G and Ibragimov, Rustam. Copula and long memory. 2008.
    • MacDonald, I.L. and Zucchini, W. Hidden Markov and Other Models for Discrete- valued Time Series: A Practical Introduction using R. Monographs on Statistics and Applied Probability. Taylor & Francis, 1997. ISBN 9780412558504.
    • Mikosch, T. and Starica, C. Limit Theory for the Sample Autocorrelations and Extremes of a Garch (1,1) Process. Preprint // Department of Mathematics, Chalmers University of Technology, Goteburg University. Department, Univ, 1998.
    • Nelsen, R. B. An Introduction to Copulas. Springer, 2007.
    • Patton, A. J. Modelling Asymmetric Exchange Rate Dependence. International Economic Review, 47(2): 527{556, 2006.
    • Pitt, Michael; Chan, David, and Kohn, Robert. E cient bayesian inference for gaussian copula regression models. Biometrika, 93(3):537{554, September 2006.
    • Rodriguez, Juan Carlos. Measuring nancial contagion: A copula approach. Journal of Empirical Finance, 14 (3):401{423, June 2007.
    • Sklar, A. Fonctions de repartition a n dimensions et leurs marges. Publications de l Institut Statistique de l'Univwesite de Paris, 8:229{31, 1959.
    • Smith, Michael S. and Khaled, Mohamad A. Estimation of copula models with discrete margins via Bayesian data augmentation. 107(497):290{303, 2012.
    • Smith, Murray D. Modelling sample selection using archimedean copulas. Econometrics Journal, 6(1):99{123, 2003.
    • Trivedi, Pravin K. and Zimmer, David M. Copula Modeling: An Introduction for Practitioners. Foundations and Trends in Econometrics, 1(1):1{111, 2006. ISSN 1551-3076.
    • Zimmer, David M. and Trivedi, Pravin K. Using trivariate copulas to model sample selection and treatment e ects: Application to family health care demand. Journal of Business & Economic Statistics, 24:63{76, 2006.
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