【SUSTech Lecture】Big data, Complex Economic System and Econometric modeling
Date: 2019-05-05


    Professor Yongmiao Hong is currently the Ernest S. Liu Professor of Economics and International Studies in the Department of Economics at Cornell University. He is also a Professor of Statistics and a field member in the Department of Statistical Science and a field member in the Center of Applied Mathematics at Cornell University. Professor Hong is a Fellow of The World Academy of Sciences (TWAS) for the Advancement of Science in Developing Countries, a Fellow of the Econometric Society and a Member and Vice Chairman of Advisory Committee for Economics Education, Chinese Ministry of Education. He served as the President of Chinese Economists Society in North America in 2009-2010.


    Professor Hong's research interests include econometric theory, time series econometrics, financial econometrics, and Chinese economy. He publishes refereed articles in mainstream economic, financial and statistical journals such as Annals of Statistics, Biometrika, Econometric Theory, Econometrica, International Economic Review, Journal of American Statistical Association, Journal of Business and Economic Statistics, Journal of Econometrics, Journal of Political Economy, Journal of Royal Statistical Society (Series B), Quarterly Journal of Economics, Review of Economic Studies, Review of Economics and Statistics, and Review of Financial Studies. He has been awarded the “Chinese Most Cited Researcher, Elsevier” for exceptional research performance in the field of economics, econometrics and finance annually for five consecutive years from 2014 to 2018. His most recent book is Probability and Statistics for Economists (World Scientific Publishing Company, 2017).


The modern economy is becoming a complex system. With the information technology being rapidly developed and widely applied, this system has been producing a vast amount of Big Data. Big Data often appears in a complex form. Big Data/Complex Data provides a lot of new and valuable information that could contribute to better understanding of the complex system, but also poses various challenges to conventional econometric modeling theories and methodologies. In this talk, I will use a variety of illustrative examples, with the “problem-oriented” approach, to discuss some possible developments in econometrics of Big Data/Complex Data in the ongoing Big Data era, including construction of sediment index and policy uncertainty index, identification of economic causal relationships and high-dimensional instrumental variable estimation, modeling interval-valued data, macroeconomic forecasts using high-dimensional leading indicators, forecasts based on time-varying predictive models, nowcasts based on data with different sampling frequencies, and time-varying moving averaging of econometric models in light of model uncertainty.