A bayesian estimation of a DSGE model with financial frictions

Episodes of crises that have recently plagued many emerging market economies have lead to a wide-spread questioning of the two traditional generations of models of currency crises. Distressed banking system and adverse credit-markets conditions have been pointed as sources of serious macroeconomics contractions, so introducing these imperfections into standard economic models can help to explain the more recent crises. This paper introduces financial frictions à la Bernanke Gertler and Gilchrist in a two-sector small open economy, suited to analyze an emerging country. The model is estimated on simulated data applying both Bayesian techniques and maximum likelihood method and comparing the results under the two di¤erent estimation procedures. First, I analyze the influence of the prior on the estimation outcomes. Results seems to confirm that one of the main advantages of Bayesian approach is the ability of providing a framework for evaluating fundamentally mis-specified models. Second, I test the sensitivity of estimation outcomes to the sample size, showing how, for large samples, results under Bayesian estimation converges asymptotically to those obtained applying maximum likelihood. A further extension would be to perform the estimation on historical data for an emerging economy that have recently experienced a financial crisis.