Methods For Nonstationary And Nonlinear Models In Economic Time Series And Their Applications

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This project concerns with theory and application of non-stationary and non-linear time series regression models. Our studies can be grouped in the three categories : Category (1) deals with both non-stationary and non-linearlity, (2) deals with non-stationary, (3) theory of regression which is the base of studies of (1) and (2). We obtained the following results.A) Concerning (1) Maekawa and Tee proposed a method of estimating a point of time of structural change in a time series when a time series has a unit root. Our result was presented at the annual meeting of Japan statistical association and MODSIM2001 held in Canberra, Australia and conferences. Our method is found to be better than other existing methods in the literature on this problem.B) Concerning (2) Maekawa and Hisamatsu studied SUR model with non-stationary regressors by asymptotic theory and Monte Carlo simulation and found that Zellner's two estimators were superior to OLS estimator as in the stationary case. This result is published in Handbook of applied econometrics and statistical inference (2002, Marcel Dekker, Inc.). Maekawa and Zongle He showed that usual F test for Granger's causality often detected spurious causality between two independent and irrelevant time series one of or both of which is/or random walk(s). This results was published in Economics letters, 2001.(C) Concerning (3) Kurata studied statistical properties of the GLS estimator in SUR model and a general linear regression model. Fukuchi studied subsampling method in time series regression.In addition we invited Professor J. Park at Seoul University to hear the recent development on non-stationary and non-linear time series. Maekawa visited the National University of Singapore to meet Professor Y. Tse to discuss non-stationary and non-linear tome series regression models in financial time series.