Publication: A Bootstrap Approach for Generalized Autocontour Testing
Loading...
Identifiers
Publication date
2016-07
Defense date
Advisors
Tutors
Journal Title
Journal ISSN
Volume Title
Publisher
Impact
Export
Abstract
We propose an extension of the Generalized Autocontour (G-ACR) tests (González-Rivera and Sun, 2015) for one-step-ahead dynamic specifications of conditional densities in-sample and of forecast densities out-of-sample. The new tests are based on probability integral transforms (PITs) computed from bootstrap conditional densities that incorporate the parameter uncertainty without assuming any particular forecast error distribution. Consequently, the parametric specification of the conditional moments can be tested without relying on any particular error distribution. We show that the asymptotic distributions of the bootstrapped G-ACR (BG-ACR) tests are well approximated using standard asymptotic distributions. Furthermore, the proposed tests are easy to implement and are accompanied by graphical tools which provide suggestions about the potential misspecification. The results are illustrated by testing the dynamic specification of the Heterogenous autoregressive (HAR) model when fitted to the popular U.S. volatility index VIX.
Description
Keywords
Distribution Uncertainty, Model Evaluation, Parameter Uncertainty, PIT