AMCA: Predictive Densities for Seemingly Unrelated Regressions with Stochastic Regressors: Forecasting Shire Level Wheat Yield by William E. Griffiths

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International Conference on Statistics, Combinatorics and Related Areas and the Eighth International Conference of Forum for Interdisciplinary Mathematics
December 19-21, 2001
School of Mathematics and Applied Statistics, University of Wollongong
Wollongong, NSW, Australia

Organizers
Satya N. Mishra (University of South Alabama), Chandra M. Gulati (University of Wollongong)

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Predictive Densities for Seemingly Unrelated Regressions with Stochastic Regressors: Forecasting Shire Level Wheat Yield
by
William E. Griffiths
University of Melbourne
Coauthors: Lisa S. Newton (University of New England), Christopher J. O'Donnell (University of New England)

Equations modeling wheat yield as a function of technological change, germination rainfall, development rainfall and flowering rainfall are specified for five shires in Western Australia. Predictive densities for end-of season yields depend on the timing of the forecast. At any given forecast time, some rainfalls will be observed and some will not. A truncated multivariate normal distribution is used to model unobserved rainfalls. Predictive densities that reflect the different levels of uncertainty in wheat-yield predictions made at four different points in time are derived for each of the five shires.

Date received: September 6, 2001

Copyright © 2001 by the author(s). The author(s) of this document and the organizers of the conference have granted their consent to include this abstract in Atlas Mathematical Conference Abstracts. Document # cagd-53.