A Comparison of the Accuracy of Asymptotic Approximations in the Dynamic Regression Model Using Kullback-Leibler Information

This paper illustrates the use of the Kullback-Leibler Information (KU) measure for assessing the relative quality of two approximations to an unknown distribution from which we can obtain simple random drawings. The illustration involves comparing the large-sample and small-disturbance asymptotic distributions under the null hypothesis of a t statistic from the dynamic linear regression model. We find very clear evidence in favour of the use of p-values and critical values from the small-disturbance Student's t distribution rather than from the large-sample standard normal distribution in this case.


Issue Date:
Aug 01 1996
Publication Type:
Working or Discussion Paper
Record Identifier:
http://ageconsearch.umn.edu/record/267910
Language:
English
Total Pages:
19
Series Statement:
Working Paper 8/96




 Record created 2018-02-06, last modified 2018-02-07

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