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asa.bayesian.01


Sponsoring Section/Society: ASA-SBSS

Session Slot: 8:30-10:20 Monday

Estimated Audience Size: 100

AudioVisual Request: Overhead Projector, Slide Projector


Session Title: Bayesian Analysis and Public and Private Policy Making

Theme Session: Yes

Applied Session: No


Session Organizer: Zellner, Arnold University of Chicago


Address: Arnold Zellner Grad. Sch. of Business U. of Chicago 1101 E. 58 St. Chicago, IL 60637

Phone: 773-702-7145

Fax: 773-702-0458

Email: arnold.zellner@gsb.uchicago.edu


Session Timing: 110 minutes total (Sorry about format):

Opening Remarks by Chair - 0 minutes First Speaker - 30 minutes Second Speaker - 30 minutes Third Speaker - 30 minutes First Discussant - 10 minutes Second Discussant - 10 minutes Floor Discusion - 0 minutes


Session Chair: Zellner, Arnold University of Chicago


Address: Arnold Zellner Graduate School of Business U. of Chicago 1101 E. 58 St. Chicago, IL 60637

Phone: 773-702-7145

Fax: 773-702-0458

Email: arnold.zellner@gsb.uchicago.edu


1. Mutual and Pension Funds Management: Beating the Markets using a Global Bayesian Investment Strategy

Quintana, Jose Mario,   CDC, Investments, New York


Address: Jose Mario Quintana CDC Investment Management Corp. 9 West 57th St., New York, NY 10019

Phone: 212-891-1823

Fax: 212-891-6113

Email: quintana@cdcna.cdcc.com

Putnam, Bluford H., CDC, Investments, New York

Abstract: There is a trend in mutual and pension funds investments toward indexing (i.e., to buy and hold "the market"). This tendency is fueled by the apparent confirmation of the efficient market hypothesis given the inability of the majority of active U.S. equity managers to beat the S&P 500 index in recent years. However, the concept of an efficient market is a relative one. In this presentation we will show a global Bayesian strategy that can generate attractive risk-adjusted excess returns by exploiting inefficiencies in the broad global markets. Furthermore, these excess returns are transportable, allowing the strategy to outperform any index, including top-performing indices such as the S&P 500.


2. What to Do When the Crystal Ball is Cloudy: Conditional and Unconditional Forecasting in Iowa

Whiteman, Charles,   University of Iowa


Address: Charles H. Whiteman Pioneer Hi-Bred Professor of Financial Economics Chair, Department of Economics W210 PBAB The University of Iowa Iowa City, IA 52242

Phone: 319-335-0831

Fax: 319-335-1956

Email: whiteman@uiowa.edu

Otrok, Christopher, University of Iowa

Abstract: Since 1990, the predictions of state tax revenues in Iowa which provide the foundation for official forecasts have been made using Bayesian methods under asymmetric linear loss. The procedures utilize vector autoregressions and uninformative priors; predictive distributions are calculated using the Monte Carlo method. Unconditional forecasts have been remarkably accurate. During policy discussions surrounding the 1997 income tax cut, conditional forecasts utilizing the methods were used to assess the usefulness of outside consultants' ``structural'' model estimates of the effects of the cut. This effort suggested that the uncertainty associated with the structural, behavioral effects of the tax cut was small relative to the inherent "reduced form" uncertainty in revenue forecasting.


3. Why use hierarchical models to assess medical profiles?

Morris, Carl,   Harvard University


Address: Carl Morris Dept. of Statistics Harvard U. Cambridge, MA 02138

Phone: 617-4965-1602

Fax: 617-495-8057

Email: morris@stat.harvard.edu

Christiansen, Cindy L., Harvard University

Abstract: Hierarchical models are being used for quality assessment more and more frequently, including for profiling medical units. In what sense are they better than simpler methods, and can we decide before fitting them whether they will produce noticeably different results? What pitfalls must be watched for, and how? Do we need to describe multi-level models and present their results more simply in order to widen their acceptance for regulatory use? The talk addresses these questions, partly through examples involving hospital comparisons.


Discussant: Polson, Nicholas   University of Chicago


Address: Nicholas Polson Grad. Sch. of Business Univ. of Chicago 1101 E. 58th St. Chicago, IL 60637

Phone: 773-702-9298

Fax: 773-702-0458

Email: nicholas.polson@gsb.uchicago.edu


Discussant: Dorfman, Jeffrey   University of Georgia


Address: Jeffrey H. Dorfman Associate Professor of Ag. & Applied Economics 315 Conner Hall University of Georgia Athens, GA 30602-7509

Phone: 706-542-0754

Fax: 706-542-0739

Email: jdorfman@agecon.uga.edu

List of speakers who are nonmembers: None


next up previous index
Next: asa.bayesian.02 Up: ASA Bayesian (3 + Previous: ASA Bayesian (3 +
David Scott
6/1/1998