Semiparametric Estimation in Simultaneous Equations of Time Series Models
AbstractA 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 regressors. It has been shown that the conventional rate is still achievable even when nonstationarity is involved in both the regressors.
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Bibliographic InfoPaper provided by University of Adelaide, School of Economics in its series School of Economics Working Papers with number 2010-26.
Length: 45 pages
Date of creation: Oct 2010
Date of revision:
Dynamic simultaneous equation; endogeneity; exogeneity; non-stationarity; partially linear model; vector semiparametric regression;
Find related papers by JEL classification:
- C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
- C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-11-06 (All new papers)
- NEP-ECM-2010-11-06 (Econometrics)
- NEP-ETS-2010-11-06 (Econometric Time Series)
- NEP-ORE-2010-11-06 (Operations Research)
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- Chaohua Dong & Jiti Gao, 2011. "Expansion of Brownian Motion Functionals and Its Application in Econometric Estimation," Monash Econometrics and Business Statistics Working Papers 19/11, Monash University, Department of Econometrics and Business Statistics.
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