OPTIMAL TESTS FOR NESTED MODEL SELECTION WITH UNDERLYING PARAMETER INSTABILITY
针对参数可能随时间变化的情况，提出了在两个嵌套模型间进行选择的最优检验方法，这些检验同时考察参数不稳定性和对参数子集的零假设，并给出了渐近分布和临界值。
This paper develops optimal tests for model selection between two nested models in the presence of underlying parameter instability. These are joint tests for both parameter instability and a null hypothesis on a subset of the parameters. They modify the existing tests for parameter instability to allow the parameter vector to be unknown. These test statistics are useful if one is interested in testing a null hypothesis on some parameters but is worried about the possibility that the parameters may be time varying. The paper provides the asymptotic distributions of this class of test statistics and their critical values for some interesting cases.I thank M. Watson for the idea of this paper and for numerous discussions, suggestions, comments, and teaching. I am grateful to T. Clark, G. Chow, R. Gallant, F. Sowell, N. Swanson, E. Tamer, and A. Tarozzi and also to the co-editor and two referees and to seminar participants at the University of Virginia, ECARES Université Libre de Brussels, the 2001 Triangle Econometrics Conference, the 2002 NBER Summer Institute, the 2003 Summer Meetings of the Econometric Society, and the 2003 EC2 Conference for comments. Financial support from IFS Summer Research, Princeton University, is gratefully acknowledged. All mistakes are mine.