Estimation of semi-varying coefficient models with nonstationary regressors
نویسندگان
چکیده
منابع مشابه
Semiparametric Estimation of Partially Linear Varying Coefficient Models with Time Trend and Nonstationary Regressors
This paper extends the partially linear varying coefficient model to contain time trend and nonstationary variables as regressors. We use the profile likelihood method to estimate both time trend coefficient in the linear component and the functional coefficients in the nonlinear component and establish their asymptotic distributions. Monte Carlo simulations are shown to investigate the finite ...
متن کاملSemiparametric Profile Likelihood Estimation of Varying Coefficient Models with Nonstationary Regressors
We study a partially linear varying coefficient model where the regressors are generated by the multivariate unit root I(1) processes. The influence of the explanatory vectors on the response variable satisfies the semiparametric partially linear structure with the nonlinear component being functional coefficients. The profile likelihood estimation methodology with the first-stage local polynom...
متن کاملEstimation in nonstationary random coefficient autoregressive models
We investigate the estimation of parameters in the random coefficient autoregressive model Xk = (φ+ bk)Xk−1 + ek, where (φ,ω 2, σ2) is the parameter of the process, Eb0 = ω2, Ee0 = σ 2. We consider a nonstationary RCA process satisfying E log |φ + b0| ≥ 0 and show that σ2 cannot be estimated by the quasi-maximum likelihood method. The asymptotic normality of the quasi-maximum likelihood estimat...
متن کاملEfficient Estimation in Heteroscedastic Varying Coefficient Models
This paper considers statistical inference for the heteroscedastic varying coefficient model. We propose an efficient estimator for coefficient functions that is more efficient than the conventional local-linear estimator. We establish asymptotic normality for the proposed estimator and conduct some simulation to illustrate the performance of the proposed method.
متن کاملOn prediction errors in regression models with nonstationary regressors
Abstract: In this article asymptotic expressions for the final prediction error (FPE) and the accumulated prediction error (APE) of the least squares predictor are obtained in regression models with nonstationary regressors. It is shown that the term of order 1/n in FPE and the term of order log n in APE share the same constant, where n is the sample size. Since the model includes the random wa...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Econometric Reviews
سال: 2015
ISSN: 0747-4938,1532-4168
DOI: 10.1080/07474938.2015.1114563