Creator: Richard, Jean François. and Zhang, Wei. Series: Simulation-based inference in econometrics Description:
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Keyword: Simulation, Econometric modeling, and Latent variables Subject (JEL): C15 - Econometric and statistical methods : General - Simulation methods and C32 - Multiple or simultaneous equation models - Time-series models ; Dynamic quantile regressions
Creator: Gourieroux, Christian, 1949, Renault, Eric., and Touzi, Nizar. Series: Simulation-based inference in econometrics Abstract:
This paper is interested in the small sample properties of the indirect inference procedure which has been previously studied only from an asymptotic point of view. First, we highlight the fact that the Andrews (1993) median-bias correction procedure for the autoregressive parameter of an AR(1) process is closely related to indirect inference; we prove that the counterpart of the median-bias correction for indirect inference estimator is an exact bias correction in the sense of a generalized mean. Next, assuming that the auxiliary estimator admits an Edgeworth expansion, we prove that indirect inference operates automatically a second order bias correction. The latter is a well known property of the Bootstrap estimator; we therefore provide a precise comparison between these two simulation based estimators.
Keyword: Edgeworth correction, Econometrics, Bootstrap, Bias correction, Economic models, Indirect inference, and Simulation Subject (JEL): C13 - Econometric and statistical methods : General - Estimation, C15 - Econometric and statistical methods : General - Simulation methods, C32 - Multiple or simultaneous equation models - Time-series models ; Dynamic quantile regressions, and C22 - Single equation models ; Single variables - Time-series models ; Dynamic quantile regressions
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.
Keyword: Dynamic equilibrium economies, Nonlinear filtering, Sequential Monte Carlo methods, and Likelihood-based inference Subject (JEL): C13 - Econometric and statistical methods : General - Estimation, C11 - Econometric and statistical methods : General - Bayesian analysis, C15 - Econometric and statistical methods : General - Simulation methods, and C10 - Econometric and statistical methods : General - General