UPDATING AND FORECASTING WITH A VARYING PARAMETER
 RECURSIVE MODEL


 J. Scott Shonkwiler


 INTRODUCTION


 Accurately forecasting prices of agricultural commodities during

 the present decade has been made difficult in light of severe shocks

 to the U.S. agricultural economy. The cattle sector has apparently

 undergone substantial disruption which was manifested, in part, by

 the sharp reduction of breeding cow inventories. In conjunction,

 quarterly average prices of Choice steers have displayed considerable

 variability during the 1970's. Specifically the run-up in Choice

 steer prices in the 1978-79 period has been unmatched by any other

 livestock price movements in recent history.

 The present study develops a four equation recursive model capable

 of forecasting Choice steer prices two quarters ahead. The model admits

 a limited varying parameter structure in an effort to capture possible

 structural change. The varying parameter technique adopted permits re-

 formulation of the model in terms of the Kalman filter time and measure-

 ment updating algroithms. Thus, updating the recursive model with recent

 data is handled systematically and forecast accuracy may be improved by

I weighting recent observations differently than the weighting that occurs


 J. SCOTT SHONKWILER is assistant professor of food and resource
 economics, University of Florida.