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In this paper, we take two different perspectives to cross-sectional dependence panel data model. We introduce the Spline-kernel method and the Quasi-Difference method to estimate the cross-sectional dependence panel data models in large n small T framework. Asymptotic distributions for the Spline-kernel estimators are derived. Monte Carlo simulations show good performances in finite samples. Empirical analysis of the impact of China's infrastructure investment on economic growth suggests that infrastructure investment and urbanization rate showed very significant positive effects on current China's economic growth. [ABSTRACT FROM AUTHOR]

The common parameter estimation methods of panel data linear model include least square dummy variable estimation, two-stage least square estimation, quasi-maximum likelihood estimation and generalized moment estimation. However, these estimation methods are not robust and are easily affected by outliers. Firstly, this paper extends support vector regression algorithm to fit several parallel super-plane simultaneously and provide a novel robust estimation of fixed-effect panel data linear model; then using the kernel trick, a robust estimation for fixed effect panel data nonlinear model is introduced. Finally, the proposed model (linear or nonlinear) is applied in forecasting air quality index of the cities of Jing-Jin-Ji district in China. Experiments shows that our proposed model are robust and have good generalization performance. [ABSTRACT FROM AUTHOR]

This paper deals with the factor modeling and forecasting for high-dimensional time series with additive outliers. Under the assumption that the sample size n and the dimension of time series p tend to infinity together, the asymptotic properties of several robust estimators are established, including estimation errors and forecast errors. We also propose a detailed algorithm of constructing bootstrap prediction intervals for the high-dimensional time series. We show the superiority of the approach by both simulation studies and an application to the daily air quality index for the main cities in the Yangtze River Delta region of China. [ABSTRACT FROM AUTHOR]

The Exchange Market Pressure Index (EMPI) is an indicator of pressure on a currency. Because of the presence of serial correlation, financial time series may not be normally distributed even for large sample sizes. They may have undefined parameters and hence parametric tests of normality may give misleading results. In this paper, we look at the time series of EMPI of eleven countries of the world, put the data to normality check using tests suggested by various scholars. We also apply a test used exclusively for serially correlated data. No one has used this test earlier. In this context, we also compare the power of these statistical tests, which is another novel contribution of this paper. On the basis of these tests the EMPI time series is found to be non-normal. Two tests are found to be the most powerful. The test which is designed exclusively for time series data is found to be powerful only for China and South Korea, the countries which had the lowest EMPI- standard- deviation in the group of all the eleven countries studied in this paper. [ABSTRACT FROM AUTHOR]

The occurrence of economic policies and other sudden and large shocks often bring out jumps in financial data, which can be characterized through continuous-time jump-diffusion model. In this paper, we will adopt convoluted smoothed approach to estimate unknown drift function of the potentially nonstationary diffusion models with jumps under high frequency sampling data. With Gaussian approximation of locally square-integrable martingales, we will establish large sample properties for the underlying nonparametric estimators. Furthermore, we construct Monte Carlo simulation study through three examples for the better finite-sample properties such as reduction of mean-squared error compared with the existing estimators. Finally, our estimator is verified through the actual data of Shibor in China for better performance. [ABSTRACT FROM AUTHOR]

This paper concerns the comparison of two sample non parametric regression. An asymptotically correct simultaneous confidence band (SCB) is proposed for the difference of two-sample non parametric regression functions to achieve the goal of comparison. Simulation experiments provide strong evidence that corroborates the asymptotic theory. The proposed SCB is used to analyze different samples of strata pressure data from the Bullianta Coal Mine in Erdos City, Inner Mongolia, China. [ABSTRACT FROM AUTHOR]