Bootstrap Conditional Distribution Tests In the Presence of Dynamic Misspecification

Arbeitspapier

In this paper, we show the first order validity of the block bootstrap in the context of Kolmogorov type conditional distribution tests when there is dynamic misspecification and parameter estimation error. Our approach differs from the literature to date because we construct a bootstrap statistic that allows for dynamic misspecification under both hypotheses. We consider two test statistics; one is the CK test of Andrews (1997), and the other is in the spirit of Diebold, Gunther and Tay (1998). The limiting distribution of both tests is a Gaussian process with a covariance kernel that reflects dynamic misspecification and parameter estimation error. In order to provide valid asymptotic critical values we suggest an extention of the empirical process version of the block bootstrap to the case of non vanishing parameter estimation error. The findings from Monte Carlo experiments show that both statistics have good finite sample properties for samples as small as 500 observations.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Paper ; No. 2003-11

Classification and Topics

• Classification: Wirtschaft ; Hypothesis Testing: General ; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
• Subject: block bootstrap ; conditional distributions ; conditional Kolmogorov tests ; dynamic misspecification ; parameter estimation error ; Bootstrap-Verfahren ; Statistische Verteilung ; Theorie ; conditional Kolmogorov test

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Corradi, Valentina ; Swanson, Norman R.
• Event: Veröffentlichung
• (who): Rutgers University, Department of Economics
• (where): New Brunswick, NJ
• (when): 2003

Further information

• Handle: http://hdl.handle.net/10419/23168
• Last update: 10.03.2025, 11:44 AM CET