A Note on Nonlinear Cointegration, Misspecification, and Bimodality

We derive the asymptotic distribution of the ordinary least squares estimator in a regression with cointegrated variables under misspecification and/or nonlinearity in the regressors. We show that, under some circumstances, the order of convergence of the estimator changes and the asymptotic distrib...

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Main Authors: Medeiros, M., Mendes, E., Oxley, Leslie
Format: Journal Article
Published: 2014
Online Access:http://hdl.handle.net/20.500.11937/48221
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author Medeiros, M.
Mendes, E.
Oxley, Leslie
author_facet Medeiros, M.
Mendes, E.
Oxley, Leslie
author_sort Medeiros, M.
building Curtin Institutional Repository
collection Online Access
description We derive the asymptotic distribution of the ordinary least squares estimator in a regression with cointegrated variables under misspecification and/or nonlinearity in the regressors. We show that, under some circumstances, the order of convergence of the estimator changes and the asymptotic distribution is non-standard. The t-statistic might also diverge. A simple case arises when the intercept is erroneously omitted from the estimated model or in nonlinear-in-variables models with endogenous regressors. In the latter case, a solution is to use an instrumental variable estimator. The core results in this paper also generalise to more complicated nonlinear models involving integrated time series. © 2014 Copyright Taylor and Francis Group, LLC.
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spelling curtin-20.500.11937-482212017-09-13T14:21:31Z A Note on Nonlinear Cointegration, Misspecification, and Bimodality Medeiros, M. Mendes, E. Oxley, Leslie We derive the asymptotic distribution of the ordinary least squares estimator in a regression with cointegrated variables under misspecification and/or nonlinearity in the regressors. We show that, under some circumstances, the order of convergence of the estimator changes and the asymptotic distribution is non-standard. The t-statistic might also diverge. A simple case arises when the intercept is erroneously omitted from the estimated model or in nonlinear-in-variables models with endogenous regressors. In the latter case, a solution is to use an instrumental variable estimator. The core results in this paper also generalise to more complicated nonlinear models involving integrated time series. © 2014 Copyright Taylor and Francis Group, LLC. 2014 Journal Article http://hdl.handle.net/20.500.11937/48221 10.1080/07474938.2012.690676 restricted
spellingShingle Medeiros, M.
Mendes, E.
Oxley, Leslie
A Note on Nonlinear Cointegration, Misspecification, and Bimodality
title A Note on Nonlinear Cointegration, Misspecification, and Bimodality
title_full A Note on Nonlinear Cointegration, Misspecification, and Bimodality
title_fullStr A Note on Nonlinear Cointegration, Misspecification, and Bimodality
title_full_unstemmed A Note on Nonlinear Cointegration, Misspecification, and Bimodality
title_short A Note on Nonlinear Cointegration, Misspecification, and Bimodality
title_sort note on nonlinear cointegration, misspecification, and bimodality
url http://hdl.handle.net/20.500.11937/48221