Creator: Fernandez-Villaverde, Jesus. and Rubio-Ramírez, Juan Francisco. Series: Joint committee on business and financial analysis Abstract:
This paper presents a method to perform likelihood-based inference in nonlinear dynamic equilibrium economies. This type of models has become a standard tool in quantitative economics. However, existing literature has been forced so far to use moment procedures or linearization techniques to estimate these models. This situation is unsatisfactory: moment procedures suffer from strong small samples biases and linearization depends crucially on the shape of the true policy functions, possibly leading to erroneous answers. We propose the use of Sequential Monte Carlo methods to evaluate the likelihood function implied by the model. Then we can perform likelihood-based inference, either searching for a maximum (Quasi-Maximum Likelihood Estimation) or simulating the posterior using a Markov Chain Monte Carlo algorithm (Bayesian Estimation). We can also compare different models even if they are nonnested and misspecified. To perform classical model selection, we follow Vuong (1989) and use the Kullback-Leibler distance to build Likelihood Ratio Tests. To perform Bayesian model comparison, we build Bayes factors. As an application, we estimate the stochastic neoclassical growth model.
关键词: Likelihood-based inference, Dynamic equilibrium economies, Sequential Monte Carlo methods, and Nonlinear filtering 学科: C15 - Statistical Simulation Methods: General, C13 - Estimation: General, C10 - Econometric and Statistical Methods and Methodology: General, and C11 - Bayesian Analysis: General
Creator: Supel, Thomas M. Series: Working paper (Federal Reserve Bank of Minneapolis. Research Department) Number: 000 描述:
This paper was published with no issue number.
关键词: Probability models, Extreme value problem, Random variables, and Truncated normal variate 学科: C10 - Econometric and Statistical Methods and Methodology: General