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

Ranjani Atukorala and Maxwell L. King

No 267910, Department of Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: 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.

Keywords: Research; Methods/Statistical; Methods (search for similar items in EconPapers)
Pages: 19
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Persistent link: https://EconPapers.repec.org/RePEc:ags:monebs:267910

DOI: 10.22004/ag.econ.267910

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