Detecting Misspecifications in Autoregressive Conditional Duration Models

33 Pages Posted: 28 Sep 2007  

Yongmiao Hong

Cornell University - Department of Economics

Yoon-Jin Lee

Kansas State University

Date Written: September 26, 2007

Abstract

We propose a new class of specification tests for Autoregressive Conditional Duration (ACD) models. Both linear and nonlinear ACD models are covered, and standardized innovations can have time-varying conditional dispersion and higher order conditional moments of unknown form. No specific estimation method is required, and the tests have a convenient null asymptotic N(0,1) distribution. To reduce the impact of parameter estimation uncertainty in finite samples, we adopt Wooldridge's (1990a) device to our context and justify its validity. Simulation studies show that the finite sample correction gives better sizes in finite samples and are robust to parameter estimation uncertainty. And, it is important to take into account time-varying conditional dispersion and higher order conditional moments in standardized innovations; failure to do so can cause strong overrejection of a correctly specified ACD model. The proposed tests have reasonable power against a variety of popular linear and nonlinear ACD alternatives.

Keywords: Autoregressive Conditional Duration, Dispersion Clustering, Finite Sample Correction, Generalized Spectral Derivative, Nonlinear Time Series, Parameter Estimation Uncertainty, Wooldridge's Device

JEL Classification: C4, C2

Suggested Citation

Hong, Yongmiao and Lee, Yoon-Jin, Detecting Misspecifications in Autoregressive Conditional Duration Models (September 26, 2007). CAEPR Working Paper No. 2007-019. Available at SSRN: https://ssrn.com/abstract=1017344 or http://dx.doi.org/10.2139/ssrn.1017344

Yongmiao Hong (Contact Author)

Cornell University - Department of Economics ( email )

Department of Statistical Science
414 Uris Hall
Ithaca, NY 14853-7601
United States
607-255-5130 (Phone)
607-255-2818 (Fax)

Yoon-Jin Lee

Kansas State University ( email )

Manhattan, KS 66506-4001
United States

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