Semiparametric Estimation in Simultaneous Equations of Time Series Models

Jiti Gao and Peter Phillips

No 2010-26, School of Economics and Public Policy Working Papers from University of Adelaide, School of Economics and Public Policy

Abstract: A system of vector 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 strictly exogenous. The parametric regressors may be stationary or nonstationary and the nonparametric regressors are nonstationary 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 least squares (SIVLS) method is proposed instead. Under certain regularity conditions, the SIVLS estimator of the parametric component is shown to be consistent with a limiting normal distribution. Interestingly, the rate of convergence in the parametric component depends on the properties of the regressorhttps://media.adelaide.edu.au/economics/e is still achievable even when nonstationarity is involved in both the regressors.

Keywords: Dynamic simultaneous equation; endogeneity; exogeneity; non-stationarity; partially linear model; vector semiparametric regression (search for similar items in EconPapers)
JEL-codes: C23 C25 (search for similar items in EconPapers)
Pages: 45 pages
Date: 2010-10
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Citations: View citations in EconPapers (2)

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