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Predictive Densities for Shire Level Wheat Yield in Western Australia

  • William E Griffiths
  • Lisa S Newton
  • Christopher J O’Donnell

Wheat yield in Western Australia (WA) depends critically on rainfall during three periods – germination, growing and flowering. The degree of uncertainty attached to a wheat-yield prediction depends on whether the prediction is made before or after the rainfall in each period has been realised. Bayesian predictive densities that reflect the different levels of uncertainty in wheat-yield predictions made at four different points in time are derived for five shires in Western Australia. The framework used for prediction is a linear regression model with stochastic regressors and inequality restrictions on the coefficients. An algorithm is developed that can be used more generally for obtaining Bayesian predictive densities in linear and nonlinear models with inequality constraints, and with or without stochastic regressors.

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Paper provided by The University of Melbourne in its series Department of Economics - Working Papers Series with number 1051.

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Length: 30 pages
Date of creation: 2008
Date of revision:
Handle: RePEc:mlb:wpaper:1051
Contact details of provider: Postal: Department of Economics, The University of Melbourne, 4th Floor, FBE Building, Level 4, 111 Barry Street. Victoria, 3010, Australia
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Web page: http://www.economics.unimelb.edu.au
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  1. Chatfield, Chris, 1993. "Calculating Interval Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(2), pages 121-35, April.
  2. Coelli, Tim J., 1992. "Forecasting Wheat Production Using Shire Level Data," 1992 Conference (36th), February 10-13, 1992, Canberra, Australia 146430, Australian Agricultural and Resource Economics Society.
  3. Chatfield, Chris, 1993. "Calculating Interval Forecasts: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(2), pages 143-44, April.
  4. Geweke, John, 1986. "Exact Inference in the Inequality Constrained Normal Linear Regression Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 1(2), pages 127-41, April.
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