Nonlinear econometric models with cointegrated and deterministically trending regressors

Yoosoon Chang (), Joon Y. Park and Peter C. B. Phillips ()

Econometrics Journal, 2001, vol. 4, issue 1, pages 1-36

Abstract: This paper develops an asymptotic theory for a general class of nonlinear non-stationary regressions, extending earlier work by Phillips and Hansen (1990) on linear coin-tegrating regressions.The model considered accommodates a linear time trend and stationary regressors, as well as multiple I(1) regressors. We establish consistency and derive the limit distribution of the nonlinear least squares estimator. The estimator is consistent under fairly general conditions but the convergence rate and the limiting distribution are critically dependent upon the type of the regression function. For integrable regression functions, the parameter estimates converge at a reduced n 1 4 rate and have mixed normal limit distributions. On the other hand, if the regression functions are homogeneous at infinity, the convergence rates are determined by the degree of the asymptotic homogeneity and the limit distributions are non-Gaussian. It is shown that nonlinear least squares generally yields inefficient estimators and invalid tests, just as in linear nonstationary regressions. The paper proposes a methodol-ogy to overcome such difficulties. The approach is simple to implement, produces efficient estimates and leads to tests that are asymptotically chi-square. It is implemented in empirical applications in much the same way as the fully modified estimator of Phillips and Hansen.

Keywords: Nonlinear regressions; Integrated time series; Nonlinear least squares; Brown-ian motion; Brownian local time. (search for similar items in EconPapers)
Date: 2001

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Working Paper: Nonlinear Econometric Models with Cointegrated and Deterministically Trending Regressors (1999)
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