Publication Type
Journal Article
Version
acceptedVersion
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
Citation
GAO, Jiti and PHILLIPS, Peter C. B..
Semiparametric Estimation in Triangular System Equations with Nonstationarity. (2013). Journal of Econometrics. 176, (1), 59-79.
Available at: https://ink.library.smu.edu.sg/soe_research/1827
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/j.jeconom.2013.04.018