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Small Sample Bias Due to Misspecification in the "Partial Adjustment" and "Adaptive Expectations" Models

Authors: Roger N. Waud;

Small Sample Bias Due to Misspecification in the "Partial Adjustment" and "Adaptive Expectations" Models

Abstract

Abstract The “partial adjustment” and “adaptive expectations” models are simply special cases of a more general model. This fact has not been commonly recognized by investigators who have used one or the other of these two models in empirical economic research. An examination of the results of small sample experiments shows that the bias in the estimated regression coefficients and the estimated mean lag which occurs when either one of the models is fitted by least squares to data generated by the more general model is quite serious. Also it is found that the spread of the distribution of these estimates is very sensitive to this misspecification. The departures from normality of these distributions are notable for the smallest sample sizes examined, but perhaps less significant in view of the severity of the other problems noted.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
12
Average
Top 10%
Average
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