Publication Type

Journal Article

Publication Date

9-2013

Abstract

A system of multivariate semiparametric nonlinear time series models is studied with possible dependence structures and nonstationarities in the parametric and nonparametric components. The parametric regressors may be endogenous while the nonparametric regressors are assumed to be strictly exogenous. The parametric regressors may be stationary or nonstationary and the nonparametric regressors are nonstationary integrated time series. Semiparametric least squares (SLS) estimation is considered and its asymptotic properties are derived. Due to endogeneity in the parametric regressors, SLS is not consistent for the parametric component and a semiparametric instrumental variable (SIV) method is proposed instead. Under certain regularity conditions, the SIV estimator of the parametric component is shown to have a limiting normal distribution. The rate of convergence in the parametric component depends on the properties of the regressors. The conventional rate may apply even when nonstationarity is involved in both sets of regressors. (C) 2013 Elsevier B.V. All rights reserved.

Keywords

Endogeneity, Integrated process, Nonstationarity, Partial linear model, Simultaneity, Vector semiparametric regression

Discipline

Econometrics

Research Areas

Econometrics

Publication

Journal of Econometrics

Volume

176

Issue

1

First Page

59

Last Page

79

ISSN

0304-4076

Identifier

10.1016/j.jeconom.2013.04.018

Publisher

Elsevier

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1016/j.jeconom.2013.04.018

Included in

Econometrics Commons

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