科学研究

Professor Xinbin Kong is a full professor of School of Statistics and Data Science, Nanjing Audit University. His research fields include high-frequency data analysis, high-dimensional data analysis and network data statistics. Professor Kong has published many research articles in leading statistical and econometric journals, such as the Annals of Statistics, Journal of the American Statistical Association, Biometrika, Journal of Econometrics, Journal of Business and Economic Statistics. He is also hosting several national natural sciences funds.

In this talk, we present a projection estimation method for large-dimensional matrix factor models with cross-sectionally spiked eigenvalues. By projecting the observation matrix onto the row or column factor space, we simplify factor analysis for matrix series to that for a lower-dimensional tensor. This method also reduces the magnitudes of the idiosyncratic error components, thereby increasing the signal-to-noise ratio, because the projection matrix linearly filters the idiosyncratic error matrix. We theoretically prove that the projected estimators of the factor loading matrices achieve faster convergence rates than existing estimators under similar conditions. Asymptotic distributions of the projected estimators are also presented. A novel iterative procedure is given to specify the pair of row and column factor numbers. Extensive numerical studies verify the empirical performance of the projection method. Two real examples in finance and macroeconomics reveal factor patterns across rows and columns, which coincide with financial, economic, or geographical interpretations.