Comparing dynamic equilibrium models to data: A Bayesian approach

Journal of Econometrics

Volumn 123, Issue 1, 2004, Pages 153-187

  Fernández Villaverde, Jesús ^a   Rubio Ramírez, Juan Francisco ^b  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b FEDERAL RESERVE BANK OF ATLANTA   (United States)

Author keywords

Bayes factors; Bayesian asymptotics; Bayesian inference; Cattle cycle; Dynamic equilibrium nodels

Indexed keywords

BAYES FACTORS; BAYESIAN ASYMPTOTICS; BAYESIAN INFERENCE; DYNAMIC EQUILIBRIUM MODELS;

ASYMPTOTIC STABILITY; INDUSTRIAL ECONOMICS; MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION; PARAMETER ESTIMATION; PROBABILITY;

FINANCIAL DATA PROCESSING;

EID: 4344706161     PISSN: 03044076     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jeconom.2003.10.031     Document Type: Article

References (58)

1. Aguirre-Torres, V., Gallant, A.R., 2001. Testing separate dynamic nonlinear econometric models. In: Schueler, G.I., Spanos, P.D. (Eds.), Monte Carlo Simulation. Balkema, Rotterdam, pp. 423-430.
2. Anderson, E.W., Hansen, L.P., McGrattan, E.R., Sargent, T.J., 1996. On the mechanics of forming and estimating dynamic linear economies. In: Amman, H.M., Kendrick, D.A., Rust, J. (Eds.), Handbook of Computational Economics, Vol. 1. Elsevier, Amsterdam.
3. Andrews D.W.K. Laws of large numbers for dependent non-identically distributed random variables. Econometric Theory. 4:1988;458-467.
4. Bureau of the Census, 1975. Historial Statistics of the United States. Colonial Times to 1970, U.S. Department of Commerce.
5. Bureau of the Census, 1989. Agricultural Statistics. U.S. Department of Commerce.
6. Chen C.F. On asymptotic normality of limiting density functions with bayesian implications. Journal of the Royal Statistical Society (Series B). 47:1985;540-546.
7. Christiano, L., Eichenbaum, M., Evans, C., 2001. Nominal rigidities and the dynamic effects of a shock to monetary policy. Working paper 01-07, Federal Reserve Bank of Cleveland.
8. Cogley, T., Sargent, T.J., 2001. Evolving post-world war II U.S. Inflation dynamics. In: Bernanke, B.S., Rogoff, K. (Eds.), NBER Macroeconomics Annual, Vol. 16. MIT Press, Cambridge, pp. 331-372.
9. Cogley, T., Sargent, T.J., 2003. Drifts and volatilities: monetary policies and outcomes in the Post World War II U.S., Working paper, Arizona State University.
10. Csiszar I. Why least squares and maximum entropy? An axiomatic approach to inference for linear inverse problems. The Annals of Statistics. 19:1991;2032-2066.
11. Davidson J. Stochastic Limit Theory. 1994;Oxford University Press, Oxford.
12. DeJong D.N., Ingam B.F., Whiteman C.H. A Bayesian approach to dynamic macroeconomics. Journal of Econometrics. 98:2000;203-223.
13. Diebold F.X., Ohanian L.E., Berkowitz J. Dynamic equilibrium economies. a framework for comparing model and data Review of Economic Studies. 65:1998;433-451.
14. Evett, I.W., 1991. Implementing Bayesian methods in forensic science. Paper presented at the Fourth Valencia International Meeting on Bayesian Statistics.
15. Fernández-Villaverde, J., Rubio-Ramírez, J.F., 2002. Estimating nonlinear dynamic equilibrium economies: a likelihood approach. Mimeo, University of Pennsylvania.
16. Gelfand A.E., Dey D.K. Bayesian model choice. asymptotics and exact calculations Journal of the Royal Statistical Society (Series B). 56:1994;501-514.
17. Gelman, A., Roberts, G.O., Gilks, W.R., 1996. Efficiente metropolis jumping rules. In: Berger, J.O., Bernado, J.M., David, A.P., Smith, A.F.M. (Eds.), Bayesian Statistics, Vol. 5. Oxford University Press, Oxford, pp. 599-607.
18. Geweke, J., 1992. Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments. In: Berger, J.O., Bernado, J.M., David, A.P., Smith, A.F.M. (Eds.), Bayesian Statistics, Vol. 4. Oxford University Press, Oxford, pp. 169-193.
19. Geweke, J., 1998. Using simulation methods for bayesian econometric models: inference, development and communication. Staff Report 249, Federal Reserve Bank of Minneapolis.
20. Geweke, J., 1999a. Simulation methods for model criticism and robustness analysis. In: Berger, J.O., Bernado, J.M., David, A.P., Smith, A.F.M. (Eds.), Bayesian Statistics, Vol. 6. Oxford University Press, Oxford, pp. 275-299.
21. Geweke, J., 1999b. Computational experiments and reality. Mimeo, University of Iowa.
22. Geweke J.M., Keane M., Runkle D. Statistical inference in the multinomial multiperiod probit model. Journal of Econometrics. 80:1997;125-165.
23. Gourieroux, C., Monfort, A., 1998. Testing non-nested hypotheses. In: Engle, R.F., McFadden, D.L. (Eds.), Handbook of Econometrics, Vol. 4. Elsevier, Amsterdam, pp. 2583-2637.
24. Hannan E.J. Multiple Time Series. 1970;Wiley, Brisbane.
25. Jacquier E., Polson N.G., Rossi P. Bayesian analysis of stochastic volatility models. Journal of Business and Economic Statistics. 12:1994;371-389.
26. Jeffreys H. The Theory of Probability. 3rd Edition:1961;Oxford University Press, Oxford.
27. Kadiyala K.R., Karlsson S. Numerical methods for estimation and inference in Bayesian VAR-models. Journal of Applied Econometrics. 12:1997;99-132.
28. Kass R.E., Raftery A.E. Bayes factors. Journal of the American Statistical Association. 90:1995;773-795.
29. Kim J. Large sample properties of posterior densities, bayesian information criterion and the likelihood principle in nonstationary time series models. Econometrica. 66:1998;359-380.
30. Kim C.J., Nelson C.R. Business cycle turning points. a new coincident index and test of duration dependence based on a dynamic factor model with regime switching Review of Economics and Statistics. 80:1988;188-201.
31. Kitamura, Y., 1998. Comparing misspecified dynamic econometric models using nonparametric likelihood. Mimeo, University of Wisconsin-Madison.
32. Landon-Lane, J., 1999. Bayesian comparison of dynamic macroeconomic models. Ph.D. Thesis, University of Minnesota.
33. Leeper, E.M., Sims, C.A., 1994. Toward a modern macroeconomic model usable for policy analysis. In: Stanley, F., Rotemberg, J.J. (Eds.), NBER Macroeconomics Annual, Vol. 9. MIT Press, Cambridge, pp. 81-118.
34. Litterman, R.B., 1980. A bayesian procedure for forecasting with vector autoregressions. Mimeo, University of Minnesota.
35. Litterman R.B. Forecasting with Bayesian vector autoregressions-five years of experience. Journal of Business and Economic Statistics. 4:1986;1-4.
36. Mengersen, K.L., Robert, C.P., Guihenneuc-Jouyaux, C., 1999. MCMC convergence diagnostics: a 'reviewww'. In: Berger, J.O., Bernado, J.M., David, A.P., Smith, A.F.M. (Eds.), Bayesian Statistics, Vol. 6. Oxford University Press, Oxford, pp. 415-440.
37. Otrok C. On measuring the welfare cost of business cycles. Journal of Monetary Economics. 47:2001;61-92.
38. Phillips P.C.B. Econometric model determination. Econometrica. 64:1996;763-812.
39. Phillips P.C.B., Ploberger W. An asymptotic theory of Bayesian inference for time series. Econometrica. 64:1996;381-412.
40. Rissanen J. Stochastic complexity and modeling. The Annales of Statistics. 14:1986;1080-1100.
41. Rosen S., Murphy K.M., Scheinkman J.A. Cattle cycle. Journal of Political Economy. 102:1994;468-492.
42. Sims C.A. Macroeconomics and reality. Econometrica. 48:1980;1-48.
43. Sims C.A., Uhlig H. Understanding unit rooters. a helicopter tour Econometrica. 59:1991;1591-1599.
44. Sims C.A., Zha T. Bayesian methods for dynamic multivariate models. International Economic Review. 39:1998;949-968.
45. Schorfheide F. Loss function-based evaluation of DGSE models. Journal of Applied Econometrics. 15:2000;645-670.
46. Shore J.E., Johnson R.W. Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy. IEEE Transactions on Information Theory. IT-26:1980;26-37.
47. Stock J.H., Watson M.W. Evidence on structural instability in macroeconomic time series relations. Journal of Business Economics and Statistics. 14:1996;11-30.
48. Stock, J.H., Watson, M.W., 2001. Macroeconomic forecasting using many predictors. Mimeo, Princeton University.
49. Veloce W., Zellner A. Modeling a competitive industry with entry. Economic Letters. 16:1984;71-75.
50. Veloce W., Zellner A. Entry and empirical demand and supply analysis for competitive industries. Journal of Econometrics. 30:1985;459-471.
51. Vuong Q.H. Likelihood ratio test for model selection and non-nested hypotheses. Econometrica. 57:1989;307-333.
52. Wald A. Note on the consistency of the maximum likelihood estimate. Annales of Mathematical Statistics. 20:1949;595-601.
53. White H. Nonlinear regression on cross-section data. Econometrica. 48:1980;721-746.
54. White H. Estimate, Inference and Specification Analysis. 1994;Cambridge University Press, Cambridge.
55. Zellner A. Basic issues in econometrics, past and present. The American Economist. 26:1982;5-10.
56. Zellner A. Optimal information processing and Bayes' theorem. American Statistician. 42:1988;278-284.
57. Zellner, A., 1989. Bayesian and non-Bayesian methods to combining models and forecast. Mimeo, University of Chicago.
58. Zellner A., Hong C., Min C.K. Forescasting turning points in international output growth rates using Bayesian exponentially weightied autoregression. Time-varying parameter, and pooling techniques. Journal of Econometrics. 49:1991;275-304.