Efficient Estimation of Copula-based Semiparametric Markov Models
This paper considers efficient estimation of copula-based semiparametric strictly stationary Markov models. These models are characterized by nonparametric invariant (one-dimensional marginal) distributions and parametric bivariate copula functions; where the copulas capture temporal dependence and tail dependence of the processes. The Markov processes generated via tail dependent copulas may look highly persistent and are useful for financial and economic applications. We first show that Markov processes generated via Clayton, Gumbel and Student's $t$ copulas and their survival copulas are all geometrically ergodic. We then propose a sieve maximum likelihood estimation (MLE) for the copula parameter, the invariant distribution and the conditional quantiles. We show that the sieve MLEs of any smooth functionals are root-$n$ consistent, asymptotically normal and efficient; and that their sieve likelihood ratio statistics are asymptotically chi-square distributed. We present Monte Carlo studies to compare the finite sample performance of the sieve MLE, the two-step estimator of Chen and Fan (2006), the correctly specified parametric MLE and the incorrectly specified parametric MLE. The simulation results indicate that our sieve MLEs perform very well; having much smaller biases and smaller variances than the two-step estimator for Markov models generated via Clayton, Gumbel and other tail dependent copulas.
|Date of creation:||Feb 2009|
|Date of revision:||Mar 2009|
|Publication status:||Published in Annals of Statistics (2009), 37(6B): 4214-4253|
|Contact details of provider:|| Postal: |
Phone: (203) 432-3702
Fax: (203) 432-6167
Web page: http://cowles.econ.yale.edu/
More information through EDIRC
|Order Information:|| Postal: Cowles Foundation, Yale University, Box 208281, New Haven, CT 06520-8281 USA|
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Xiaohong Chen & Roger Koenker & Zhijie Xiao, 2009.
"Copula-based nonlinear quantile autoregression,"
Royal Economic Society, vol. 12(s1), pages S50-S67, 01.
- Xiaohong Chen & Roger Koenker & Zhijie Xiao, 2008. "Copula-Based Nonlinear Quantile Autoregression," Cowles Foundation Discussion Papers 1679, Cowles Foundation for Research in Economics, Yale University.
- Xiaohong Chen & Roger Koenker & Zhijie Xiao, 2008. "Copula-Based Nonlinear Quantile Autoregression," Boston College Working Papers in Economics 691, Boston College Department of Economics.
- Xiaohong Chen & Roger Koenker & Zhijie Xiao, 2008. "Copula-based nonlinear quantile autoregression," CeMMAP working papers CWP27/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Andrew Patton, 2004.
"Modelling Asymmetric Exchange Rate Dependence,"
wp04-04, Warwick Business School, Finance Group.
- Chen, Xiaohong & Fan, Yanqin & Tsyrennikov, Viktor, 2006.
"Efficient Estimation of Semiparametric Multivariate Copula Models,"
Journal of the American Statistical Association,
American Statistical Association, vol. 101, pages 1228-1240, September.
- Xiaohong Chen & Yanqin Fan & Victor Tsyrennifov, 2004. "Efficient Estimation of Semiparametric Multivariate Copula Models," Vanderbilt University Department of Economics Working Papers 0420, Vanderbilt University Department of Economics.
- Chunrong Ai & Xiaohong Chen, 2003. "Efficient Estimation of Models with Conditional Moment Restrictions Containing Unknown Functions," Econometrica, Econometric Society, vol. 71(6), pages 1795-1843, November.
- Chen, Jian & Peng, Liang & Zhao, Yichuan, 2009. "Empirical likelihood based confidence intervals for copulas," Journal of Multivariate Analysis, Elsevier, vol. 100(1), pages 137-151, January.
- Jianqing Fan & Jiancheng Jiang, 2007. "Nonparametric inference with generalized likelihood ratio tests," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 16(3), pages 409-444, December.
- Xiaohong Chen & Xiaotong Shen, 1998. "Sieve Extremum Estimates for Weakly Dependent Data," Econometrica, Econometric Society, vol. 66(2), pages 289-314, March.
- Nze, Patrick Ango & Doukhan, Paul, 2004. "Weak Dependence: Models And Applications To Econometrics," Econometric Theory, Cambridge University Press, vol. 20(06), pages 995-1045, December.
- repec:ner:tilbur:urn:nbn:nl:ui:12-86725 is not listed on IDEAS
- Gao, Jiti, 2007. "Nonlinear time series: semiparametric and nonparametric methods," MPRA Paper 39563, University Library of Munich, Germany, revised 01 Sep 2007.
- Chen, Xiaohong & Fan, Yanqin, 2006. "Estimation of copula-based semiparametric time series models," Journal of Econometrics, Elsevier, vol. 130(2), pages 307-335, February.
- Beare, Brendan, 2008. "Copulas and Temporal Dependence," University of California at San Diego, Economics Working Paper Series qt2880q2jq, Department of Economics, UC San Diego.
- Genest, C. & Werker, B.J.M., 2001. "Conditions for the asymptotic semiparametric efficiency of an omnibus estimator of dependence parameters in copula models," Other publications TiSEM b733c3f4-38d2-49aa-a2c7-4, Tilburg University, School of Economics and Management.
- Koenker, Roger & Xiao, Zhijie, 2006. "Quantile Autoregression," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 980-990, September.
- Xiaotong Shen & Hsin-Cheng Huang & Jimmy Ye, 2004. "Inference After Model Selection," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 751-762, January.
When requesting a correction, please mention this item's handle: RePEc:cwl:cwldpp:1691. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Glena Ames)
If references are entirely missing, you can add them using this form.