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.
- “Forecasting Volatility Using Double Shrinkage Methods,” (with Mingmian Cheng and Xiye Yang), 2021, Journal of Empirical Finance, 62, 46-61.
- “Testing for Jumps and Jump Intensity Path Dependence,” (with Valentina Corradi and Mervyn J. Silvapulle), 2018, Journal of Econometrics, 204, 248-267.
- “Big Data Analytics In Economics: What Have We Learned So Far, And Where Should We Go From Here?,” (with Weiqi Xiong), 2018, Canadian Journal of Economics, 3, 695-746.
- “Robust Forecast Comparison,” (with Sainan Jin and Valentina Corradi), 2017, Econometric Theory, 33, 1306-1351.
- “Testing for Structural Stability of Factor Augmented Forecasting Models,” (with Valentina Corradi), Journal of Econometrics, 182, 2014, 100-118.
- “Forecasting Financial and Macroeconomic Variables Using Data Reduction Methods: New Empirical Evidence,” (with Hyun Hak Kim), Journal of Econometrics, 178, 2014, 352-367.
- “Predictive Inference for Integrated Volatility", (with Valentina Corradi and Walter Distaso), Journal of American Statistical Association, 106, 2011, 1496-1512.
- “Consistent Estimation With a Large Number of Weak Instruments,” (with John C. Chao), Econometrica, 73, 2005, 1673-1692.