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A simple test for parameter constancy in a nonlinear time series regression model

Authors
Journal
Economics Letters
0165-1765
Publisher
Elsevier
Publication Date
Volume
38
Issue
2
Identifiers
DOI: 10.1016/0165-1765(92)90047-3

Abstract

Abstract This paper develops a test for parameter constancy in a nonlinear regression model where the parameters follow a random walk under the alternative. The test is derived assuming normality, and supposing that a first order Taylor series expansion of the nonlinear function holds valid. This yields a test with a simple stucture and tractable asymptotic distribution. Simulation evidence suggests that the test performs well in more general circumstances.

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