Abstract
Relationships between crop yield or livestock production and associated weather experienced have generally been estimated by way of single equation regression procedures. Where a number of crops and livestock activities are undertaken concurrently in a region, or individual estimates are required for one or more activities across a set of discrete regions, separate estimation of the individual yield relationships is likely to be statistically inefficient, since the residuals from each equation are likely to be correlated. This is because they will in each case include the effects of aspects of weather which are not correlated with the weather indices utilized as independent variables in the regressions. In this situation, provided the independent variables are not all common to all equations, Zellner's (1962) seemingly unrelated estimation procedure provides a statistically efficient means of jointly estimating the set of yield-weather relationships. An application of this methodology which compares it with the usual least squares approach is presented.
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