Estimation of integrated squared spectral density derivatives

Kernel spectrum estimates are used for the estimation of integrals of various squared derivatives of a spectral density. Rates of convergence in mean squared error are calculated, which show that the parametric rate of convergence n-1 can be achieved with some smoothness conditions on the spectral density function. The implications for data-driven bandwidth selection in kernel spectral density estimation are considered.Integrated squared derivative kernel spectrum estimate rate of convergence

Maximum Eigenvalue Test for Seasonal Cointegrating Ranks

The maximum eigenvalue (ME) test for seasonal cointegrating ranks is presented using the approach of Cubadda ["Oxford Bulletin of Economics and Statistics" (2001), Vol. 63, pp. 497-511], which is computationally more efficient than that of Johansen and Schaumburg ["Journal of Econometrics" (1999), Vol. 88, pp. 301-339]. The asymptotic distributions of the ME test statistics are obtained for several cases that depend on the nature of deterministic terms. Monte Carlo experiments are conducted to evaluate the relative performances of the proposed ME test and the trace test, and we illustrate these tests using a monthly time series. Copyright 2006 Blackwell Publishing Ltd.

Generalized method of moments estimation for cointegrated vector autoregressive models

In this study, a generalized method of moments (GMM) for the estimation of nonstationary vector autoregressive models with cointegration is considered. Two iterative methods are considered: a simultaneous estimation method and a switching estimation method. The asymptotic properties of the GMM estimators of these methods are found to be the same as those of the Gaussian reduced-rank estimator. Through Monte Carlo simulation, the small-sample properties of the GMM estimators are studied and compared with those of the Gaussian reduced-rank estimator and the maximum likelihood estimator considered by other researchers. In the case of small samples, the GMM estimators are more robust to deviations from normality assumptions, particularly to outliers.Cointegration GMM estimation VAR model

Unit root tests for seasonal models with deterministic trends

We develop "Dickey-Fuller type" test statistics for seasonal unit roots when a model is fitted with deterministic seasonal trends. The asymptotic distributions of the test statistics are derived, and the asymptotic power of these statistics under a sequence of local alternatives are considered. Empirical percentiles of the test statistics for selected seasonal periods are provided. The power and size of the test statistics are examined for finite samples through a Monte Carlo simulation and compared with those of the Lagrange multiplier test of Ahn and Cho (1993a).Seasonal unit roots Deterministic trends Brownian motions Nearly nonstationary