Nonlinear Cointegrating Regression under Weak Identification
An asymptotic theory is developed for a weakly identiﬁed cointegrating regression model in which the regressor is a nonlinear transformation of an integrated process. Weak identiﬁcation arises from the presence of a loading coeﬀicient for the nonlinear function that may be close to zero. In that case, standard nonlinear cointegrating limit theory does not provide good approximations to the ﬁnite sample distributions of nonlinear least squares estimators, resulting in potentially misleading inference. A new local limit theory is developed that approximates the ﬁnite sample distributions of the estimators uniformly well irrespective of the strength of the identiﬁcation. An important technical component of this theory involves new results showing the uniform weak convergence of sample covariances involving nonlinear functions to mixed normal and stochastic integral limits. Based on these asymptotics, we construct conﬁdence intervals for the loading coeﬀicient and the nonlinear transformation parameter and show that these conﬁdence intervals have correct asymptotic size. As in other cases of nonlinear estimation with integrated processes and unlike stationary process asymptotics, the properties of the nonlinear transformations aﬀect the asymptotics and, in particular, give rise to parameter dependent rates of convergence and diﬀerences between the limit results for integrable and asymptotically homogeneous functions.
Shi, Xiaoxia and Phillips, Peter C.B., "Nonlinear Cointegrating Regression under Weak Identification" (2010). Cowles Foundation Discussion Papers. 2105.