Best subset selection of autoregressive models with exogenous variables and generalized autoregressive conditional heteroscedasticity errors

Journal of the Royal Statistical Society. Series C: Applied Statistics

Volumn 55, Issue 2, 2006, Pages 201-224

  So, Mike K P ^a   Chen, Cathy W S ^b   Liu, Feng Chi ^b  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

^b FENG CHIA UNIVERSITY   (Taiwan)

Author keywords

Autoregressive models with exogenous variables; Bayesian methods; Generalized autoregressive conditional heteroscedasticity models; Markov chain Monte Carlo methods; Stochastic search variable selection; Stock markets

Indexed keywords

EID: 33644624381     PISSN: 00359254     EISSN: 14679876     Source Type: Journal    
DOI: 10.1111/j.1467-9876.2006.00535.x     Document Type: Article

References (29)

1. Baillie, R. T., Bollerslev, T. and Mikkelsen, H. O. (1996) Fractionally integrated generalized autoregressive conditional heteroskedasticity. J. Econometr., 74, 3-30.
2. Bauwens, L. and Lubrano, M. (1998) Bayesian inference on G ARCH models using the Gibbs sampler. Econometr. J., 1, C23-C46.
3. Bodie, Z. (1976) Common stocks as a hedge against inflation. J. Finan., 31, 459-470.
4. Bollerslev, T. (1986) Generalized autoregressive conditional heteroskedasticity. J. Econometr., 31, 307-327.
5. Bollerslev, T., Chou, R. Y. and Kroner, K. F. (1992) ARCH modeling in finance: a review of the theory and empirical evidence. J. Econometr., 52, 5-59.
6. Bollerslev, T. and Mikkelsen, H. O. (1996) Modelling and pricing long memory in stock market volatility. J. Econometr., 73, 151-184.
7. Box, G. E. P. and Tiao, G. C. (1973) Bayesian Inference in Statistical Analysis. Reading: Addison-Wesley.
8. Chen, C. W. S. (1992) Bayesian analysis of bilinear time series models: a Gibbs sampling approach. Communs Statist. Theory Meth., 21, 3407-3425.
9. Chen, C. W. S. (1999) Subset selection of autoregressive time series models. J. Forecast., 18, 505-516.
10. Chen, C. W. S. and Lee, J. C. (1995) Bayesian inference of threshold autoregressive models. J. Time Ser. Anal., 16, 483-492.
11. Chib, S. and Greenberg, E. (1995) Understanding the Metropolis-Hastings algorithm. Am. Statistn, 49, 327-335.
12. Ding, Z. and Granger, C. W. J. (1996) Modeling volatility persistence of speculative returns: a new approach. J. Econometr., 73, 185-215.
13. Ding, Z., Granger, C. W. J. and Engle, R. F. (1993) A long memory property of stock market returns and a new model. J. Empir. Finan., 1, 83-106.
14. Engle, R. F. (1982) Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50, 987-1008.
15. Gelman, A., Roberts, G. O. and Gilks, W. R. (1996) Efficient Metropolis jumping rules. In Bayesian Statistics 5 (eds J. M. Bernardo, J. O. Berger, A. P. Dawid and A. F. M. Smith), pp. 599-607. Oxford: Oxford University Press.
16. George, E. I. and McCulloch, R. E. (1993) Variable selection via Gibbs sampling. J. Am. Statist. Ass., 88, 881-889.
17. Glosten, L. R., Jagannathan, R. and Runkle, D. E. (1993) On the relation between the expected value and the volatility of the nominal excess return on stocks. J. Finan., 48, 1779-1801.
18. Hastings, W. K. (1970) Monte-Carlo sampling methods using Markov chains and their applications. Biometrika, 57, 97-109.
19. Karolyi, G. A. (1995) A multivariate GARCH model of international transmissions of stock returns and volatility: the case of the United States and Canada. J. Bus. Econ. Statist., 13, 11-25.
20. Khil, J. and Lee, B. S. (2000) Are common stocks a good hedge against inflation?: evidence from the Pacific-rim countries. Pacif. Bas. Finan. J., 8, 457-482.
21. Kim, B. J. C. and Ghazali, N. A. (1998) The liquidity effect of money shocks on short-term interest rates: some international evidence. Int. Econ. J., 12, 49-63.
22. Ling, S. (1999) On the probabilistic properties of a double threshold ARMA conditional heteroskedastic model. J. Appl. Probab., 36, 688-705.
23. Marriott, J., Ravishanker, N., Gelfand, A. and Pai, J. (1996) Bayesian analysis for ARMA processes: complete sampling-based inference under exact likelihoods. In Bayesian Analysis in Statistics and Econometrics: Essays in Honor of Arnold Zellner (eds D. A. Berry, K. M. Chaloner and J. K. Geweke), pp. 243-256. New York: Wiley.
24. McCulloch, R. E. and Tsay, R. S. (1993) Bayesian inference and prediction for mean and variance shifts in autoregressive time series. J. Am. Statist. Ass., 88, 968-978.
25. McCulloch, R. E. and Tsay, R. S. (1994a) Bayesian analysis of autoregressive time series via the Gibbs sampler. J. Time Ser. Anal., 15, 235-250.
26. McCulloch, R. E. and Tsay, R. S. (1994b) Statistical inference of economic time series via Markov switching models. J. Time Ser. Anal., 15, 523-539.
27. Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N. and Teller, A. H. (1953) Equations of state calculations by fast computing machines. J. Chem. Phy., 21, 1087-1091.
28. Sellin, P. (2001) Monetary policy and the stock market: theory and empirical evidence. J. Econ. Surv., 15, 491-541.
29. Tierney, L. (1994) Markov chains for exploring posterior distribution (with discussion). Ann. Statist., 22, 1701-1762.