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A Bootstrap Approach for Generalized Autocontour Testing

Helena Veiga (), Esther Ruiz, Gloria Gonzalez-Rivera () and Joao Henrique Gonçalves Mazzeu

DES - Working Papers. Statistics and Econometrics. WS from Universidad Carlos III de Madrid. Departamento de Estadística

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.

Keywords: Distribution; Uncertainty; Model; Evaluation; Parameter; Uncertainty; PIT (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-for
Date: 2016-07
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