Simultaneous Testing of Mean and Variance Structures in Nonlinear Time Series Models

This paper proposes a nonparametric simultaneous test for parametric specification of the conditional mean and variance functions in a time series regression model. The test is based on an empirical likelihood (EL) statistic that measures the goodness of fit between the parametric estimates and the nonparametric kernel estimates of the mean and variance functions. A unique feature of the test is its ability to distribute natural weights automatically between the mean and the variance components of the goodness of fit. To reduce the dependence of the test on a single pair of smoothing bandwidths, we construct an adaptive test by maximizing a standardized version of the empirical likelihood test statistic over a set of smoothing bandwidths. The test procedure is based on a bootstrap calibration to the distribution of the empirical likelihood test statistic. We demonstrate that the empirical likelihood test is able to distinguish local alternatives which are different from the null hypothesis at an optimal rate.Bootstrap, empirical likelihood, goodness{of{t test, kernel estimation, least squares empirical likelihood, rate-optimal test

A test for model specification of diffusion processes

We propose a test for model specification of a parametric diffusion process based on a kernel estimation of the transitional density of the process. The empirical likelihood is used to formulate a statistic, for each kernel smoothing bandwidth, which is effectively a Studentized $L_2$-distance between the kernel transitional density estimator and the parametric transitional density implied by the parametric process. To reduce the sensitivity of the test on smoothing bandwidth choice, the final test statistic is constructed by combining the empirical likelihood statistics over a set of smoothing bandwidths. To better capture the finite sample distribution of the test statistic and data dependence, the critical value of the test is obtained by a parametric bootstrap procedure. Properties of the test are evaluated asymptotically and numerically by simulation and by a real data example.Comment: Published in at http://dx.doi.org/10.1214/009053607000000659 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Growth Engine: Effects of China’s Trade and Investment on the Economies of East and Southeast Asia

The emergence of China as an economic superpower through globalization and fragmentation of production has impacted global trade relations, particularly in East and Southeast Asia (ESA). China has become a major trading partner for ESA economies not only through exporting goods to ESA countries, but also importing goods to satisfy China’s energy and consumer needs. This thesis studies the impact trade and investment relationships with China have on ESA economies. This study will include ten developed and developing ESA countries: Japan, Republic of Korea, Singapore, Thailand, Malaysia, Indonesia, Vietnam, Cambodia, Lao DPR, and Philippines. The results obtained include: 1) the effects that exports to China and inbound FDI from China have on ESA countries’ real GDP per capita, 2) the impact of trade and investment with China depends on the countries’ degree of advancement, 3) emphasis on the importance of regional cooperation in Asia