Bayesian analysis of econometric time series models using hybrid integration rules

Communications in Statistics - Theory and Methods

Volumn 31, Issue 1, 2002, Pages 49-72

  Gamerman, Dani ^a   Moreira, Ajax R B ^b  

^a FEDERAL UNIVERSITY OF RIO DE JANEIRO   (Brazil)

^b INSTITUTO DE PESQUISA ECONÔMICA APLICADA   (Brazil)

Author keywords

Bayesian; Dynamic; Hyperparameters; Impulse response; Markov chain Monte Carlo; Metropolis Hastings algorithm; Vector autoregressive models

Indexed keywords

EID: 31244433870     PISSN: 03610926     EISSN: None     Source Type: Journal    
DOI: 10.1081/STA-120002434     Document Type: Article

References (31)

1. Harvey, A.C. Forecasting, Structural Time Series Models and the Kalman Filter, Cambridge University Press, Cambridge, 1989.
2. Hamilton, J. Time Series Analysis, Princeton University Press, New Jersey, 1994.
3. Carlin, P.B.; Poison, N.G.; Stoffer, D.S. A Monte Carlo approach to nonnormal and nonlinear state-space modeling. Journal of the American Statistical Association 1992, 87, 493-500.
4. Carter, C.K.; Kohn, R. On Gibbs sampling for state space models. Biometrika 1994, 81, 541-553.
5. Fruhwirth-Schnatter, S. Data augmentation and dynamic linear models. Journal of Time Series Analysis 1994, 75, 183-202.
6. Chib, S.; Greenberg, E. Hierarchical analysis of SUR models with extensions to correlated serial errors and time-varying parameter models. Journal of Econometrics 1995, 68, 339-360.
7. Shephard, N.; Pitt, M.K. Likelihood analysis of non-Gaussian measurement time series. Biometrika 1997, 84, 653-667.
8. Gamerman, D. Markov chain Monte Carlo for dynamic generalized linear models. Biometrika 1998, 85, 215-227.
9. Kadiyala, K.R.; Karlsson, S. Numerical methods for estimation and inference in Bayesian VAR-models. Journal of Applied Econometrics 1997, 12, 99-132.
10. Lopes, H.F.; Moreira, A.R.B.; Schmidt, A.M. Hyperparameter estimation in forecast models. Computational Statistics & Data Analysis 1999, 29, 387-410.
11. Sims, C.A.; Zha, T. Error bands for impulse responses. Cowles Foundation discussion paper no. 1085, 1995.
12. Schmidt, A.M.; Gamerman, D.; Moreira, A.R.B. An adaptive resampling scheme for cycle estimation. Journal of Applied Statistics 1999, 26, 619-641.
13. West, M.; Harrison, P.J. Bayesian Forecasting and Dynamics Models, 2nd ed., Springer-Verlag, New York, 1997.
14. Kitagawa, G.; Gersch, W. Smoothness Priors Analysis of Time Series, Lecture Notes in Statistics, 116, Springer-Verlag, New York, 1996.
15. Chan, K.S.; Tong, H. On estimating thresholds in autoregressive models. Journal of Time Series Analysis 1986, 7, 179-190.
16. Terasvirta, T.; Anderson, H.M. Characterizing nonlinearities in business cycles using smooth transition autoregressive models. Journal of Applied Econometrics 1992, 7, S119-S132.
17. Rubin, D.R. Using the SIR algorithm to simulate posterior distributions (with discussion), in Bayesian Statistics 3 (eds J. M. Bernardo et al.), Oxford University Press, Oxford, pp. 395-402, 1988.
18. King, R.G.; Plosser, C.I.; Stock, J.H.; Watson, M.H. Stochastic trends and economic fluctuation. The American Economic Review 1981, 81, 819-840.
19. Mellander, E.; Vredin, A.; Warne, A. Stochastic trends and economic fluctuation in a small open economy. Journal of Applied Econometrics 1992, 7, 269-394.
20. Gamerman, D. Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Chapman & Hall, London, 1997.
21. Gilks, W.R., Richardson, S., Spiegelhalter, D.J. (Eds.) Markov Chain Monte Carlo in Practice, Chapman & Hall, London, 1996.
22. Gamerman, D. Sampling from the posterior distribution in generalized linear mixed models. Statistics and Computing 1997, 7, 57-68.
23. Gelman, A.; Rubin, D.R. Inference from iterative simulation using multiple sequences (with discussion). Statistical Science, 1992, 7, 457-511.
24. Geweke, J. Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments (with discussion), in Bayesian Statistics 4 (eds. J. M. Bernardo et al.), 169-193, University Press: Oxford, 1992.
25. Cristiano, L.M.; Eichenbaum, M.; Evans, C. The effect of monetary policy shocks: evidence from the flow of funds. Review of Economics and Statistics 1994, 78, 16-34.
26. Sims, C.A. Are forecasting models usable for policy analysis. Quarterly Review of the Federal Reserve Bank of Minneapolis, 1986, 2-16.
27. Sims, C.A.; Zha, T. Bayesian methods for dynamic multivariate models. Technical Report, Yale University, 1996.
28. Einchenbaum, E.; Evans, C. Some empirical evidence on the effects of monetary policy shocks on exchange rates. Quarterly Journal of Economics 1993, 975-1009.
29. Fiorencio, A.; Moreira, A.R.B. Measuring the stability of the price system. Economic Modelling 2001, 18, 381-397.
30. Campbell, J.Y.; Perron, P. Pitfalls and oportunities: what macroeconomist should know about unit roots, NBER working papers, 1991.
31. Moreira, A.R.B. PRV for Windows manual. Technical Report, IPEA, Brazil, 1997.