Publication Date: June 2009
Linear cointegration is known to have the important property of invariance under temporal translation. The same property is shown not to apply for nonlinear cointegration. The requisite limit theory involves sample covariances of integrable transformations of non-stationary sequences and time translated sequences, allowing for the presence of a bandwidth parameter so as to accommodate kernel regression. The theory is an extension of Wang and Phillips (2008) and is useful for the analysis of nonparametric regression models with a misspeciﬁed lag structure and in situations where temporal aggregation issues arise. The limit properties of the Nadaraya-Watson (NW) estimator for cointegrating regression under misspeciﬁed lag structure are derived, showing the NW estimator to be inconsistent with a “pseudo-true function” limit that is a local average of the true regression function. In this respect nonlinear cointegrating regression diﬀers importantly from conventional linear cointegration which is invariant to time translation. When centred on the pseudo-function and appropriately scaled, the NW estimator still has a mixed Gaussian limit distribution. The convergence rates are the same as those obtained under correct speciﬁcation but the variance of the limit distribution is larger. Some applications of the limit theory to non-linear distributed lag cointegrating regression are given and the practical import of the results for index models, functional regression models, and temporal aggregation are discussed.
Dynamic misspeciﬁcation, Functional regression, Integrable function, Integrated process, Local time, Misspeciﬁcation, Mixed normality, Nonlinear cointegration, Nonparametric regression
JEL Classification Codes: C22, C32
See CFP: 1374