Bayesian model uncertainty in smooth transition autoregressions

Journal of Time Series Analysis

Volumn 27, Issue 1, 2006, Pages 99-117

  Lopes, Hedibert F ^a   Salazar, Esther ^a  

^a NONE

Author keywords

Deviance information criterion; Markov Chain Monte Carlo; Model selection; Nonlinear time series model; Reversible jump MCMC

Indexed keywords

EID: 33645151774     PISSN: 01439782     EISSN: 14679892     Source Type: Journal    
DOI: 10.1111/j.1467-9892.2005.00455.x     Document Type: Article

References (46)

1. ABANTO-VALLE, C., MIGON H., and LOPES, H. F. (2004) Bayesian modelling of financial returns: a relationship between volatility and trading volume. In Proceedings of the XIV Brazilian Meeting of Econometrics Paraiba Brazil.
2. AKAIKE, H. (1974) New look at the statistical model identification. IEEE Transactions in Automatic Control AC 19, 716-23.
3. BACON, D. and WATTS, D. (1971) Estimating the transition between two intersecting straight lines. Biometrika 58, 525-34.
4. BERG, A., MEYER, R. and Yu, J. (2004) DIC as a model comparison criterion for stochastic volatility models. Journal of Business and Economic Statistics 22, 107-20.
5. BESAG, J. (1989) A candidate's formula - a curious result in Bayesian prediction. Biometrika 76(1), 183.
6. CAMPBELL, E. (2004). Bayesian selection of threshold autoregressive models. Journal of Time Series Analysis 25, 467-82.
7. CARVALHO, C. and LOPES, H. F. (2004) Simulation-based sequential analysis of Markov switching stochastic volatility models. Working Paper, Graduate School of Business, University of Chicago, Chicago, IL.
8. CHAN, K. S. and TONG, H. (1986) On estimating thresholds in autoregressive models. Journal of Time Series Analysis 7, 179-90.
9. CHEN C. (1998). A Bayesian analysis of generalized threshold autoregressive models. Statistics and Probability Letters 40, 15-22.
10. CHEN, C. and LEE, J. (1995) Bayesian inference of threshold autoregressive models. Journal of Time Series Analysis 16, 483-92.
11. CHEN, R. and TSAY, R. (1991) On the ergodicity of TAR (1) processes. Annals of Applied Probability 1, 613-34.
12. CLYDE M. (1999) Bayesian model averaging and model search strategies (with discussion). In Bayesian Statistics 6 (eds J. BERNARDO, A. DAWID, J. BERGER and A. SMITH). Oxford: Oxford University Press, pp. 157-85.
13. VAN DIJK, D., TERÄSVIRTA T. and FRANSES, P. (2002) Smooth transition autoregressive models-a survey of recent developments. Econometrics Reviews 21, 1-47.
14. GAMERMAN D. (1997) Markov Chain Monte Carlo: Stochastic simulation for Bayesian inference. London: Chapman and Hall.
15. GELFAND, A. E. and SMITH, A. M. F. (1990) Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association 85, 398-409.
16. GEWEKE, J., TERUI, N. (1993) Bayesian threshold autoregressive models for nonlinear time series. Journal of Time Series Analysis, 14, 441-54.
17. GILKS, W. R., RICHARDSON, S. and SPIEGELHALTER, D. J. (eds.) (1996) Markov Chain Monte Carlo in Practice (3rd edn). London: Chapman and Hall.
18. GREEN, P. J. (1995) Reversible Jump Markov chain Monte Carlo computation Bayesian model determination. Biometrika 57(1), 97-109.
19. HAMILTON J. (1989) A new approach to the economic analysis of nonstationary time series and business cycle. Econometrica 57, 357-84.
20. HASTINGS, W. R. (1970) Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57, 97-109.
21. HOETING, J. A., MADIGAN, D., RAFTERY, A. E. and VOLINSKY, C. T. (1999) Bayesian model averaging: a tutorial. Statistical Science 14, 401-4.
22. HUERTA, G. and LOPES, H. (2000) Bayesian forecasting and inference in latent structure for the brazilian industrial production index. Brazilian Review of Econometrics 20, 1-26.
23. KOOP, G. and POTTER S. (1999) Bayes factor and nonlinearity: evidence from economic time series. Journal of Econometrics 88, 251-81.
24. KOOP, G. and POTTER, S. (2003) Bayesian analysis of endogenous delay threshold models. Journal of Business and Economic Statistics 21, 93-103.
25. LOPES, H. F. and MIGON H. (2002) Comovements and contagion in emergent markets: stock indexes volatilities. Case Studies in Bayesian Statistics 6, 285-300.
26. LOPES, H. F., AGUILAR, O. and WEST, M. (2000) Time-varying covariance structures in currency markets. In Proceedings of the XXII Brazilian Meeting of Econometrics, Campinas, Brazil.
27. LUBRANO, M. (2000) Bayesian analysis of nonlinear time-series models with a threshold. In Nonlinear Econometric Modeling in Time Series: Proceedings of the Eleventh International Symposium in Economic Theory (eds W. A. BARNETT, D. F. HENDRY, S. HYLLEBERO, T. TERASVIRTA, D. TJOSTHEIM and A. W. WURTZ). Cambridge University Press, 2000.
28. LUUKKONEN, R. SAIKKONEN, P. and TERÄSVIRTA, T. (1988) Testing linearity against smooth transition autoregressive models. Biometrika 75, 491-9.
29. MEDEIROS, M. C. and VEIGA, A. (2000) A flexible coefficient smooth transition time series model. Working Paper Series in Economics and Finance 360, Stockholm School of Economics.
30. METROPOLIS, N., ROSENBLUTH, A. W., ROSENBLUTH, M. N., TELLER, A. H. and TELLER E. (1953) Equation of state calculations by fast computing machine. Journal of Chemical Physics 21, 1087-91.
31. OZAKI, T. (1982) The statistical analysis of perturbed limit cycle process using nonlinear time series models. Journal of time-series Analysis 3, 29-41.
32. PETRUCCELLI, J. and WOOLFORD, S. (1984) A threshold AR(1) model. Journal of Applied Probability 21, 270-86.
33. RAFTERY, A. E., MADIGAN, D. and HOETING, J. A. (1997) Bayesian model averaging for linear regression models. Journal of the American Statistical Association 92, 179-191.
34. ROBERT, C. and CASELLA, G. (1999) Monte Carlo Statistical Methods. New York: Springer-Verlag.
35. SCHWARZ, G. (1978) Estimating the dimension of a model. Annals of Statistics 6, 461-4.
36. SO, M. and CHEN, C. (2003) Subset threshold autoregression. Journal of Forecasting 22, 49-66.
37. SPIEGELHALTER, D., BEST, N., CARLIN, B. and VAN DER LINDE A. (2002) Bayesian measures of model complexity and fit (with discussion). Journal of The Royal Statistical Society 64, 583-639.
38. TERÄSVIRTA T. (1994) Specification, estimation, and evaluation of smooth transition autoregressive models. Journal of the American Statistical Association 89(425), 208-18.
39. TONG, H. (1978) On a threshold model. In Pattern Recognition and Signal Processing (ed. C. CHENG). Amsterdam: Sijthoff and Noordhoff, pp. 101-41.
40. TONO, H. (1990) Non-linear Time Series: A Dynamical Systems Approach, Volume 6 of Oxford Statistical Science Series. Oxford: Oxford University Press.
41. TONG, H. and LIM K., (1980) Threshold autoregressions, limit cycles, and data. Journal of the Royal Statistical Society 42, 245-92.
42. TROUGHTON, P. and GODSILL, S. (1997) A reversible jump sampler for autoregressive time series, employing full conditionals to achieve efficient model space moves. Technical CUED/F-INFENG/TR.304, Department of Engineering, University of Cambrigde, Cambrigde.
43. TSAY, R. (1989) Testing and modeling threshold autoregressive processes. Journal of the American Statistical Association 84, 431-52.
44. WONG, C. and Li, W. (1998) A note on the corrected Akaike information criterion for threshold autoregressive models. Journal of Time Series Analysis 19, 113-24.
45. XIA, Y. and Li, W. K. (1999) On single-index coefficient regression models. Journal of the American Statistical Association 94, 1275-85.
46. ZHU, L. and CARLIN, B. (2000) Comparing hierarquical models for spatio-temporally misaligned data using the deviance information criterion. Statistics in Medicine 19, 2265-78.