Template-Type: ReDIF-Paper 1.0
Series: Tinbergen Institute Discussion Papers
Creation-Date: 2021-11-18
Number: 21-098/III
Author-Name: F. Blasques
Author-Workplace-Name: Vrije Universiteit Amsterdam, Tinbergen Institute
Author-Name: P. Gorgi
Author-Workplace-Name: Vrije Universiteit Amsterdam, Tinbergen Institute
Author-Name: S.J. Koopman
Author-Workplace-Name: Vrije Universiteit Amsterdam, Tinbergen Institute, CREATES Aarhus University
Title: Conditional Score Residuals and Diagnostic Analysis of Serial Dependence in Time Series Models
Abstract: We introduce conditional score residuals and provide a general framework for the diagnostic analysis of time series models. A key feature of conditional score residuals is that they account for the shape of the conditional distribution. These residuals offer reliable and powerful diagnostic tools for testing residual autocorrelation. Furthermore, they can be employed in models of which it is not clear how to define residuals. The asymptotic properties of the empirical autocorrelation function for conditional score residuals are formally derived. The results yield a unified theory for the diagnostic analysis of a wide class of time series models. The practical relevance of the proposed framework is illustrated for heavy-tailed GARCH models. Monte Carlo and empirical results support the finding that conditional score residuals are more reliable in testing residual autocorrelation, when compared to squared GARCH residuals. We finally show how a diagnostic analysis can be designed for dynamic copula models.
Classification-JEL: C21, C22, C58
Keywords: conditional score residuals, diagnostic analysis, residual autocorrelation, time series models
File-URL: https://papers.tinbergen.nl/21098.pdf
File-Format: application/pdf
File-Size: 500.133 bytes
Handle: RePEc:tin:wpaper:20210098