Creator: Geweke, John. Series: Working paper (Federal Reserve Bank of Minneapolis. Research Dept.) Number: 526 Keyword: Simulation, Monte Carlo, and Econometrics Subject (JEL): C15 - Econometric and statistical methods : General - Simulation methods and C63 - Mathematical methods and programming - Computational techniques ; Simulation modeling
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: Diebold, Francis X., 1959- and Schuermann, Til. Series: Simulation-based inference in econometrics Abstract:
The possibility of exact maximum likelihood estimation of many observation-driven models remains an open question. Often only approximate maximum likelihood estimation is attempted, because the unconditional density needed for exact estimation is not known in closed form. Using simulation and nonparametric density estimation techniques that facilitate empirical likelihood evaluation, we develop an exact maximum likelihood procedure. We provide an illustrative application to the estimation of ARCH models, in which we compare the sampling properties of the exact estimator to those of several competitors. We find that, especially in situations of small samples and high persistence, efficiency gains are obtained.
Keyword: Econometrics, Observation-driven models, ARCH models, Estimation, and Exact maximum likelihood estimation Subject (JEL): C22 - Single equation models ; Single variables - Time-series models ; Dynamic quantile regressions