This paper shows how the empirical entropy (also known as exponential likelihood or non-parametric tilting) method can be used to test general parametric hypothesis in time series regressions. To capture the weak dependence of the observations, the paper uses blocking techniques which are also used in the bootstrap literature on time series. Monte Carlo evidence suggests that the proposed test statistics have better finite-sample properties than conventional test statistics such as the Wald statistic. Copyright 2005 Blackwell Publishing Ltd.
Download Info
To download:
If you experience problems downloading a file, check if you have the
proper application to
view it first. Information about this may be contained
in the File-Format links below. In case of further problems read
the IDEAS help
page. Note that these files are not on the IDEAS
site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
For technical questions regarding this item, or to correct its listing, contact: (Christopher F. Baum).
Related research
Keywords:
Cited by: (explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)
Did you know? You can create a compilation of all publications of a group of people, say alumni of a program, your students or memers of an association.