This paper investigates the performances of GMM estimates using kernel methods with and without prewhitening and the VARHAC method in a representative agent exchange economy. A Monte Carlo study is conducted to evaluate the issues of estimating the spectral density functions, e.g., parametric...

This paper investigates the performances of GMM estimates using kernel methods with and without prewhitening and the VARHAC method in a representative agent exchange economy. A Monte Carlo study is conducted to evaluate the issues of estimating the spectral density functions, e.g., parametric...

In this paper, we apply the asymptotic theory of panel cointegration developed by Kao and Chiang (1998) 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...

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...

In this paper, we study the asymptotic distributions for least-squares (OLS), fully modified (FM), and dynamic OLS\ (DOLS) estimators in cointegrated regression models in panel data. We show that the OLS, FM, and DOLS estimators are all asymptotically normally distributed. However, the...

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...

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...

Wavelet analysis is a new mathematical tool developed as a unified field of science over the last decade. As spatially adaptive analytic tools, wavelets are useful for capturing serial correlation where the spectrum has peaks or kinks, as can arise from persistent/strong dependence, seasonality...

A new class of specification tests is proposed to detect for neglected nonlinearity and dynamic misspecification in panel models. The tests can detect a wide range of model misspecifications while being robust to conditional heteroskedasticity and higher order time-varying moments of unknown...