Detecting Misspecifications in Autoregressive Conditional Duration Models

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.

Number of Pages in PDF File: 33

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

working papers series