Predictive densities for models with stochastic regressors and inequality constraints: Forecasting local-area wheat yield

Griffiths, William E., Newton, Lisa S. and O'Donnell, Christopher J. (2010) Predictive densities for models with stochastic regressors and inequality constraints: Forecasting local-area wheat yield. International Journal of Forecasting, 26 2: 397-412. doi:10.1016/j.ijforecast.2009.12.008


Author Griffiths, William E.
Newton, Lisa S.
O'Donnell, Christopher J.
Title Predictive densities for models with stochastic regressors and inequality constraints: Forecasting local-area wheat yield
Journal name International Journal of Forecasting   Check publisher's open access policy
ISSN 0169-2070
Publication date 2010-04
Year available 2010
Sub-type Article (original research)
DOI 10.1016/j.ijforecast.2009.12.008
Volume 26
Issue 2
Start page 397
End page 412
Total pages 16
Editor Kajal Lahiri
Gael Martin
Place of publication Amsterdam, The Netherlands
Publisher Elsevier
Collection year 2011
Language eng
Subject C1
140201 Agricultural Economics
140303 Economic Models and Forecasting
140302 Econometric and Statistical Methods
Abstract Forecasts from regression models are frequently made conditional on a set of values for the regressor variables. We describe and illustrate how to obtain forecasts when some of those regressors are stochastic and their values have not yet been realized. The forecasting device is a Bayesian predictive density which accommodates variability from an unknown error term, uncertainty from unknown coefficients, and uncertainty from unknown stochastic regressors. We illustrate how the predictive density of a forecast changes as more regressors are observed and therefore fewer are unobserved. An example where the local-area wheat yield depends on the rainfall during three periods–germination, growing and flowering–is used to illustrate the methods. Both a noninformative prior and a prior with inequality restrictions on the regression coefficients are considered. The results show how the predictive density changes as more rainfall information becomes available.
Keyword bayesian forecasting
inequality restrictions
random regressors
rainfall distributions
truncated distributions
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Available online 2 February 2010. -- Special Issue: Bayesian Forecasting in Economics

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2011 Collection
School of Economics Publications
 
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Created: Wed, 10 Mar 2010, 18:05:22 EST by Alys Hohnen on behalf of School of Economics