Abstract :

In this paper we develop full Bayesian inference for a rich class of DSGE models (Dynamics Stochastic General Equilibrium). It is well known that any DSGE model after being log-linearized could be written as a state space model with Gaussian shocks in the state equation, in this context we propose to explore a DSGE with structure variance on the shocks, building a normal model with stochastic volatility and a Student-t model with stochastic volatility. The methodologies are applied to real data and is compared with the traditional approach.

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Keywords :

Bayesian inference, DSGE models, state-space model, stochastic volatility, structure variance.

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References :

1. Asai, M., McAleer, M. and Yu, J. 2006. Multivariate stochastic volatility: A review. Econometric Reviews, 25, 145-175.
2. Barnard, J., McCulloch, R. and Meng, X. 2000. Modeling covariance matrices in terms of standard deviations and correlations, with applications to shrinkage. Statistica Sinica, 10, 1281-1311.
3. Blanchard, O. and Kahn, C. 1980. The solution of linear difference models under rational expectations. Econometrica, 48, 1305-1311.
4. Chib, S., Omori, Y. and Asai, M. 2009. Multivariate stochastic volatility. Handbook of Financial Time Series, 365-400.
5. Chib, S. and Ramamurthy, S. 2014. DSGE models with student-t errors. Econometrics Reviews, 33, 152-171.
6. Cruz-Torres, C. and Migon, H. 2025. DSGE model with non structure variance. Brazilian Journal of Development, 11(2), 1-21.
7. Curdia, V., Del Negro, M. and Greenwald, D. 2014. Rare shocks, great recessions. Journal of Applied Econometrics, 29, 1031-1052.
8. Fernandez-Villaverde, J., Guerron-Quintana, P. and Rubio-Ramirez, F. 2015. Estimating dynamic equilibrium models with stochastic volatility. Journal of Econometrics, 185, 216-229.
9. Fonseca, T., Ferreira, M. and Migon, H. 2008. Objective bayesian analysis for the Student-t regression model. Biometrika, 95, 325-333.
10. Ireland, P. 2004. Technology shocks in the new keynesian model. The Review of Economics and Statistics, 86, 923-936.
11. Jacquier, E., Polson, N. and Rossi, P. 2004. Bayesian analysis of stochastic volatility models with fat-tails and correlated errors. Journal of Econometrics, 122, 185-212.
12. Justiniano, A. and Primiceri, G. 2008. The time-varying volatility of macroeconomic fluctuations. American Economic Review, 98, 604-641.
13. Kass, R. and Raftery, A. 1995. Bayes factor. Journal of the American statistical association, 90, 773-795.
14. Klein, P. 2000. Using the generalized Schur form to solve a multivariate linear rational expectations model. Journal of Economic Dynamics and Control, 24, 1405-1423.
15. Uhlig, H. 1999. A toolkit for analyzing nonlinear dynamic stochastic models easily. Computational Methods for the Study of Dynamic Economies.
16. Yu, J. and Meyer, R. 2006. Multivariate stochastic volatility models: Bayesian estimation and model comparison. Econometric Reviews, 25, 361-384.