A Simple Automatic Portmanteau Test for Conditional Goodness-of-Fit in Dynamic Models Zaichao Du Southwestern University of Finance and Economics, China Juan Carlos Escanciano Indiana University, Bloomington, USA April 4, 2012 Abstract In this paper, we propose a data-driven Portmanteu test for conditional goodness- of-t in dynamic models. Our method uses the well-known fact that under the correct specication of the conditional distribution the generalized errors obtained after the conditional probability integral transformation are iid U [0; 1]. The proposed test is a modied Box-Pierce statistic applied to the generalized errors, with a data-driven choice for the number of autocorrelations used. The test explicitly takes into account of the parameter estimation e/ect, and as a result it has a convenient standard chi-squared limit distribution. Hence, the main distinctive feature of our approach is its simplicity. The basic methodology is extended to conditional models for the tail, conditional hazard models and di/usion models. It is shown that, unlike existing approaches, our approach is applicable to a wide class of models, including ARMA-GARCH models with time varying higher order moments, such as Hansens (1994) skewed t model. A simulation study shows that our test has a satisfactory size and power performance. Finally, an empirical application to the Nikkei Index data highlights the merits of the proposed test over competing alternatives. Keywords and Phrases: Autocorrelation; Box-Pierce; Goodness-of-Fit; GARCH; Parameter estimation uncertainty; Skewed t distribution. JEL Classications: C12, C58, C52. 1