Dan  Hamilton

Dan Hamilton, Ph.D.

Director and Associate Professor, MS in Quantitative Economics program and Director, Economics, Center for Economic Research and Forecasting

dhamilto@callutheran.edu
(805) 493-3724
CLU Westlake Center

Office Hours: Monday afternoons, Thursday afternoon, or by appointment.




Dan Hamilton, Associate Professor in the School of Management, is the Director of Economics for the CLU Center for Economic Research and Forecasting (CERF), and is the Director of CLU's Master of Science in Quantitative Economics (MSQE).  In partnership with CERF Director Matthew Fienup, he is a member of the Wall Street Journal's Economic Forecasting Survey, and has more than 23 years of experience in economic forecasting. 

Dan was the Principal Investigator of the 2019 and recently completed 2020 Latino GDP Report, an in-depth computation of Latino GDP for the United States that utilizes Input-Output analysis as well as analysis of a large number of detailed U.S. economic datasets. CERF has computed economic forecasts for the United States, California, Oregon, Los Angeles County, the San Fernando Valley, Ventura County, and for various other counties and cities in California. CERF has built a variety of custom forecast models for its clients, including demographic, long-run, General Fund, and detailed product-SKU forecast models. Dan worked with Bill Watkins in 2009 to launch CERF and its sister academic program, MSQE, upon arrival at CLU in 2009.

The MSQE program focuses on teaching the application of quantitative methods in applied economic and financial analysis, including economic forecasting. The program is ranked 7th in the U.S. for Financial Economics, and has earned the Certified Forecast Trainer designation from the premier academic forecasting society, the International Institute of Forecasters.

Prior to CLU, 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 County of Ventura, The Towbes Group, the Sares-Regis Group, among many others. 

Prior to UCSB, Dan worked for three years for the Wharton Econometric Forecasting Associates (WEFA Group) where he built economic forecast models and interpreted forecasts for a wide variety of clients including Visa International, the Panama Canal Authority, and the United States government.  He also conducted intensive forecast training programs for both internal and external clients at the WEFA Group.  

Hamilton earned a B.S. degree in agricultural economics from UC Davis and his M.A. 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. The San Fernando Valley Economy. The Ventura County Economy.

Time-Series Econometrics. Macroeconomic Theory. Input-Output Analysis and Economic Measurement. Business Cycle Analysis. 

Eviews Model Programming. AREMOS Model Programming.

CLU Speakers Bureau and Experts Directory Page, click here.

Principal Investigator, LDC U.S. Latino GDP Report: Quantifying the New American Economy, co-authored with M. Fienup, D. Hayes-Bautista, and P. Hsu. Latino Donors Collaborative (LDC), September 2020. Link.

Principal Investigator, LDC U.S. Latino GDP Report: Quantifying the New American Economy, co-authored with M. Fienup, D. Hayes-Bautista, and P. Hsu. Latino Donors Collaborative (LDC), September 2019.

FM-OLS Estimation of Near Unit Root data, Cal Lutheran Economics Department, Working Paper, January 30, 2019.

Monetary Policy and PID Control, Journal of Economic Interaction and Coordination, DOI 10.1007/s11403-014-0127-3, March 2014.  

http://link.springer.com/article/10.1007/s11403-014-0127-3

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 R. Isaac and K. Lesh, Business Economics, Vol. 45, No. 1, January 2010.  http://www.nabe.com/publib/be/1001/index.html

 

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