UCSD economic forecasting conference convenes leading thinkers for issue resolution


Twenty of the world's leading econometricians will convene, for a wide-ranging discussion of economic forecasting issues and challenges April 16-17 at the University of California, San Diego. The conference, under the auspices of the Rady School of Management at UCSD, will be highlighted by a special symposium and Nobel Laureate Dinner, honoring the recipient of the 2003 Nobel Prize, Clive Granger, and the 1990 winner, Harry M. Markowitz, both UCSD faculty members.

Reporters and members of the public are invited to attend the symposium, from 4:00 p. m. to 6:00 p.m. on April 16, followed by a reception and the Nobel Laureate Dinner. All events will take place at the Salk Institute near the UCSD campus. The full conference gets underway on Friday morning at 7:30 a. m. and concludes at 5 p. m. on Saturday, April 17.

"Advances in economic forecasting have revised and enhanced our ability to deal with major issues in global business and personal life. We are fortunate to have the participation of the world's leading authorities in our conference and have high expectations for the work that will be accomplished and the lasting materials that will be one of our most important products," said Robert Sullivan, Dean of the Rady School of Management and the host for the conference, symposium and dinner.

The two-day meeting is directly linked to preparation of The Handbook of Economic Forecasting, to be published by Elsevier/North Holland, with materials emerging from the conference to be edited by UCSD Economics professors Graham Elliott, Clive Granger and Allan Timmerman. Conference topics include: volatility modeling in finance; forecasting of market data; forecasting with large data sets and numerous additional challenges in economic forecasting.

Econometric forecasting involves using data over time such as monthly US interest rates from 1982 to 2004 or daily stock prices of a company over the past two years, or a firm's sales figures over the last decade to come up with a model that can detect patterns to predict future values. The process requires paying careful attention to which variables have predictive value and evaluating various models for their efficacy.

Source: Eurekalert & others

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