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Full-Text Articles in Mathematics
International R&D Spillovers: An Application Of Estimation And Inference In Panel Cointegration, Chihwa Kao, Min-Hsien Chiang, Bangtian Chen
International R&D Spillovers: An Application Of Estimation And Inference In Panel Cointegration, Chihwa Kao, Min-Hsien Chiang, Bangtian Chen
Center for Policy Research
In this paper, we apply the asymptotic theory of panel cointegration developed by Kao and Chiang (1997) to Coe and Helpman's (1995) international R&D spillovers regression. The OLS with bias-correction, the fully-modified (FM) and the dynamic OLS (DOLS) estimations produce different predictions about the impact of foreign R&D on total factor productivity (TFP), although all the estimations support the result that domestic R&D is related to TFP.
A Monte Carlo Comparison Of Tests For Cointegration In Panel Data, Chihwa Kao
A Monte Carlo Comparison Of Tests For Cointegration In Panel Data, Chihwa Kao
Center for Policy Research
This paper surveys recent developments and provides Monte Carlo comparison on various tests proposed for cointegration in panel data. In particular, tests for two panel models, varying intercepts and varying slopes, and varying intercepts and common slopes are presented from the literature with a total of seven tests being simulated. In all cases, results on empirical size and size-adjusted power are given.
On The Estimation And Inference Of A Cointegrated Regression In Panel Data, Chihwa Kao
On The Estimation And Inference Of A Cointegrated Regression In Panel Data, Chihwa Kao
Center for Policy Research
The main contribution of this paper is to add to the literature by suggesting a dynamic OLS (DOLS) estimator and providing a serious comparison of the finite sample properties of the OLS, fully modified OLS (FMOLS), and DOLS estimators in panel cointegrated regression models. Monte Carlo results illustrate the sampling behavior of the proposed estimators and show that (1) the OLS estimator has a non-negligible bias in finite samples, (2) the FMOLS estimator does not improve over the OLS estimator in general, and (3) the DOLS outperforms both the OLS and FMOLS estimators.
On The Estimation Of A Linear Time Trend Regression With A One-Way Error Component Model In The Presence Of Serially Correlated Errors, Chihwa Kao, Jamie Emerson
On The Estimation Of A Linear Time Trend Regression With A One-Way Error Component Model In The Presence Of Serially Correlated Errors, Chihwa Kao, Jamie Emerson
Center for Policy Research
In this paper we study the limiting distributions for ordinary least squares (OLS), fixed effects (FE), first difference (FD), and generalized least squares (GLS) estimators in a linear time trend regression with a one-way error component model in the presence of serially correlated errors. We show that when the error term is I(0), the FE is asymptotically equivalent to the GLS. However, when the error term is I(1) the GLS could be less efficient than the FD or FE estimators, and the FD is the most efficient estimator. However, when the intercept is included in the model and the error …