Short Biography



Norman R. Swanson was educated at the University of Waterloo and the University of California, San Diego. He is Professor in the Economics Department at Rutgers University. He has held previous positions at Pennsylvania State University, Texas A&M University, and Purdue University, and IBM Canada. His primary research interests include financial econometrics, forecasting, machine learning and big data, and time series analysis. He is a fellow of the Journal of Econometrics (the top field journal in econometrics) and the International Association of Applied Econometrics, and he currently serves or has served as editor for various scholarly journals including the Journal of Econometrics, Journal of Business and Economic Statistics, and the International Journal of Forecasting. He is a member of various professional organizations, including the Econometric Society, the American Statistical Association, the American Economic Association, and the Canadian Economic Association. He is on the steering committees of the M6 forecasting competition as well as various conferences and symposia. He has published over 100 peer reviewed articles in leading economics and statistics journals including Econometrica, Journal of Econometrics, Review of Economics and Statistics, Journal of Business and Economic Statistics, and the Journal of the American Statistical Association, among others. He is or has been a visiting scholar and consultant to various central banks, universities, and inter-governmental organizations including the University of Maryland, the University of Pennsylvania, Surrey University, Humbolt University, the Federal Reserve Bank of Philadelphia, the Bank of Canada, and the International Monetary Fund, among others. He has acted as a consultant and expert witness for the last 25 years, consulting for firms ranging from the Union Bank of Switzerland and the Bank of Zurich, to DFA Capital Management, Inc. and Conning, Inc., and has acted as expert witness and carried out expert analysis in numerous property casualty cases, including multiple cases involving financial services companies in which forecasting and but-for analysis was undertaken.