Working Paper: CEPR ID: DP12934
Authors: Enrique Sentana; Gabriele Fiorentini
Abstract: We propose generalised DWH specification tests which simultaneously compare three or more likelihood-based estimators of conditional mean and variance parameters in multivariate conditionally heteroskedastic dynamic regression models. Our tests are useful for GARCH models and in many empirically relevant macro and finance applications involving VARs and multivariate regressions. To design powerful and reliable tests, we determine the rank deficiencies of the differences between the estimators' asymptotic covariance matrices under the null of correct specification, and take into account that some parameters remain consistently estimated under the alternative of distributional misspecification. Finally, we provide finite sample results through Monte Carlo simulations.
Keywords: Durbin-Wu-Hausman Tests; Partial Adaptivity; Semiparametric Estimators; Singular Covariance Matrices
JEL Codes: C12; C14; C22; C32; C52
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| type of estimator (C51) | consistency of parameter estimates (C51) |
| nongaussian maximum likelihood estimators (C51) | inconsistent parameter estimates (C51) |
| semiparametric estimators (C51) | reliability of parameter estimates (C51) |
| choice of estimator (C51) | validity of empirical results (C90) |