Risultati della ricerca
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.
Parola chiave: Exact maximum likelihood estimation, Observation-driven models, ARCH models, Estimation, and Econometrics Soggetto: C22 - Single equation models ; Single variables - Time-series models ; Dynamic quantile regressions
Creator: Berkowitz, Jeremy, Diebold, Francis X., 1959-, and Ohanian, Lee E. Series: Staff report (Federal Reserve Bank of Minneapolis. Research Department) Number: 243 Abstract:
We propose a constructive, multivariate framework for assessing agreement between (generally misspecified) dynamic equilibrium models and data, a framework which enables a complete second-order comparison of the dynamic properties of models and data. We use bootstrap algorithms to evaluate the significance of deviations between models and data, and we use goodness-of-fit criteria to produce estimators that optimize economically relevant loss functions. We provide a detailed illustrative application to modeling the U.S. cattle cycle.
Soggetto: C52 - Model Evaluation, Validation, and Selection, C22 - Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes, and C14 - Semiparametric and Nonparametric Methods: General