TY - JOUR
T1 - Bootstrap conditional distribution tests in the presence of dynamic misspecification
AU - Corradi, Valentina
AU - Swanson, Norman R.
N1 - Funding Information:
We are grateful to the organizers (Jean-Marie Dufour and Benoit Perron), as well as the participants of the 2001 C.R.D.E. Conference on Resampling Methods in Econometrics, Université de Montréal for providing many useful comments and suggestions. Additionally, we would like to thank two anonymous referees, Walter Distaso, Marcelo Fernandes, Sílvia Gonçalves, Atsushi Inoue, Lutz Killian, Shinichi Sakata, Paolo Zaffaroni, and seminar participants at Brunel University, Cambridge University, ECARES-ULB, University of Nottingham, and University of Southampton for their helpful comments. Parts of this paper were written while Swanson was visiting the economics department at University of California, San Diego, and he would like to thank the econometrics group there for providing a stimulating research environment within which to work. Corradi gratefully acknowledges financial support from the ESRC (Grant code R000230006) and Swanson has benefited from the support of Rutgers University in the form of a Research Council grant.
PY - 2006/8
Y1 - 2006/8
N2 - In this paper, we show the first order validity of the block bootstrap for Kolmogorov-type conditional distribution tests under dynamic misspecification and parameter estimation error. Our approach is unique because we construct statistics that allow for dynamic misspecification under both hypotheses. We consider two tests; the CK test of Andrews [1997. A conditional Kolmogorov test, Econometrica 65, 1097-1128], and a version of the DGT test of Diebold, Gunther and Tay [1998a. Evaluating density forecasts with applications to finance and management. International Economic Review 39, 863-883]. Test limiting distributions are Gaussian processes with covariance kernels that reflect dynamic misspecification and parameter estimation error. Critical values are based on an extension of the empirical process version of the block bootstrap to the case of nonvanishing parameter estimation error. Monte Carlo experiments are also carried out.
AB - In this paper, we show the first order validity of the block bootstrap for Kolmogorov-type conditional distribution tests under dynamic misspecification and parameter estimation error. Our approach is unique because we construct statistics that allow for dynamic misspecification under both hypotheses. We consider two tests; the CK test of Andrews [1997. A conditional Kolmogorov test, Econometrica 65, 1097-1128], and a version of the DGT test of Diebold, Gunther and Tay [1998a. Evaluating density forecasts with applications to finance and management. International Economic Review 39, 863-883]. Test limiting distributions are Gaussian processes with covariance kernels that reflect dynamic misspecification and parameter estimation error. Critical values are based on an extension of the empirical process version of the block bootstrap to the case of nonvanishing parameter estimation error. Monte Carlo experiments are also carried out.
KW - Block bootstrap
KW - Conditional Kolmogorov tests
KW - Conditional distributions
KW - Dynamic misspecification
KW - Parameter estimation error
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U2 - 10.1016/j.jeconom.2005.06.013
DO - 10.1016/j.jeconom.2005.06.013
M3 - Article
AN - SCOPUS:33745882987
SN - 0304-4076
VL - 133
SP - 779
EP - 806
JO - Journal of Econometrics
JF - Journal of Econometrics
IS - 2
ER -