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...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/48221 |
| Summary: | 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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