Dan Hamilton, an assistant professor in the School of Management at California Lutheran University, is the director of economics for the CLU Center for Economic Research and Forecasting (CERF). With more than 17 years of experience in working with economic forecast models, Hamilton worked for three years for the Wharton Econometric Forecasting Associates (WEFA Group) where he produced and interpreted forecasts for a wide variety of clients including Visa International, the Panama Canal Authority, and the Central Bank of Netherlands Antilles.
Hamilton joined the UCSB Economic Forecast Project in 2000 where he worked with well-known regional organizations including Vandenberg Air Force Base, the County of Santa Barbara, The Towbes Group, Ojai Sanitation District, among others. He built and maintained a variety of forecast models in EViews, including models of the United States, California, and Oregon. He also built several forecast models in Excel, including models of grade-school enrollment and commuting.
In 2009, Hamilton along with partners Bill Watkins, Ph.D., and Kirk Lesh, joined the CLU faculty and established the CLU CERF. They also serve as the faculty and directors for the University’s new M.S. in Economics program that focuses on teaching the applied tools for economic forecasting.
Hamilton earned a B.S. degree in agricultural economics from UC Davis and his M.S. and Ph.D. in economics from University of California Santa Barbara.
A.S. Mathematics and Physical Sciences, American River College
B.S. (honors), Agricultural Economics, University of California, Davis
M.A. Economics, University of California, Santa Barbara (fields: Finance and Econometrics)
Ph.D. Economics, University of California, Santa Barbara (fields: Macroeconomics and Econometrics)
The United States Economy. The California Economy.
Time-Series Econometrics. Macroeconomic Theory. Business Cycle Analysis.
Eviews Model Programming. AREMOS Model Programming.
Monetary Policy and PID Control, Journal of Economic Interaction and Coordination, DOI 10.1007/s11403-014-0127-3, March 2014.
Forecasting with Structural Models and VARs: Relative Advantages and the Client Connection, Foresight: The International Journal of Applied Forecasting, Issue 22, Fall 2011. http://ideas.repec.org/a/for/ijafaa/y2011i23p37-42.html
Using Aggregate Time Series Variables to Forecast Notices of Default, co-authored with Rani Isaac and Kirk Lesh, Business Economics, Vol. 45, No. 1, January 2010. http://www.nabe.com/publib/be/1001/index.html