Nonlinear regression for unit root models with autoregressive errors

Chang Sik Kim and In-Moo Kim

Economics Letters, 2008, vol. 100, issue 3, 326-329

Abstract: This paper shows that the nonlinear least squares estimator for unit root models has the limiting distribution free of nuisance parameters and is more efficient than the augmented Dickey-Fuller estimator when the sum of coefficients for lagged variables is negative.

Date: 2008
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:100:y:2008:i:3:p:326-329

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