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Unstable Models from Incorrect Forms
Author(s) -
Alston Julian M.,
Chalfant James A.
Publication year - 1991
Publication title -
american journal of agricultural economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.949
H-Index - 111
eISSN - 1467-8276
pISSN - 0002-9092
DOI - 10.2307/1242445
Subject(s) - autocorrelation , econometrics , monte carlo method , parametric statistics , specification , parametric model , statistical hypothesis testing , mathematics , computer science , statistics
Parametric tests for structural change are conditional on the joint hypothesis of functional form and other aspects of the model specification. This problem is often disregarded. Monte Carlo evidence using three data sets indicates that apparently innocuous specification errors can lead to substantial increases in the probability of finding structural change when it is not present in the data‐generating mechanism. Significant Chow tests and autocorrelation are much more likely when the wrong functional form is used. Maximum Chow tests falsely reject stable preferences much more often than their nominal size suggests, even when the correct model is estimated.