Working Paper: CEPR ID: DP12682
Authors: Gabriele Fiorentini; Enrique Sentana
Abstract: We characterise the mean and variance parameters that distributionally misspecified maximum likelihood estimators can consistently estimate in multivariate conditionally heteroskedastic dynamic regression models. We also provide simple closed-form consistent estimators for the rest. The inclusion of means and the explicit coverage of multivariate models make our procedures useful not only for GARCH models but also in many empirically relevant macro and finance applications involving VARs and multivariate regressions. We study the statistical properties of our proposed consistent estimators, as well as their efficiency relative to Gaussian pseudo maximum likelihood procedures. Finally, we provide finite sample results through Monte Carlo simulations.
Keywords: consistency; efficiency; misspecification
JEL Codes: C13; C22; C32; C51
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| nongaussian pseudo maximum likelihood estimators (C51) | consistent estimates of mean and variance parameters (C51) |
| nongaussian pseudo maximum likelihood estimators (C51) | reliable results in empirical applications (C51) |
| nongaussian pseudo maximum likelihood estimators (C51) | efficiency and consistency in estimating parameters (C51) |
| misspecification of the underlying distribution (C46) | potential compromise of estimator efficiency (C51) |