We propose a specification test of a parametrically specified model against a weakly specified alternative. The latter is estimated using K nonparametric nearest neighbors (K-NN) in the context of an artificial regression. We derived the asymptotic distribution under the null hypothesis and under a series of local alternatives. Monte Carlo simulations suggest that the test is quite powerful although it has a tendency to over-reject under the null hypothesis.
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Paper provided by Queen's University, Department of Economics in its series Working Papers with number
778.
Cited by: (explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)
Russell Davidson & James G. MacKinnon, 2001.
"Artificial Regressions,"
Working Papers
1038, Queen's University, Department of Economics.
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Other versions:
Russell Davidson & James G. MacKinnon, 1999.
"Artificial Regressions,"
Working Papers
978, Queen's University, Department of Economics.
[Downloadable!]