Autoregressive Spatial Interpolation and Agricultural Modelling: a Programming Approach
نویسندگان
چکیده
Research in the field of agricultural policy often calls for an explicit representation of geographical aspects of the country or region under consideration. Furthermore, in connection with environmental issues one has to account for spatial interactions, for example in connection with water flows or erosion processes. However, this inevitably leads to the problem that all data needed for estimation are not available at all points. The paper addresses this issue and presents, for a spatial model, an interpolation procedure by maximum likelihood estimation. In this model, the spatial interactions are represented through an autoregressive scheme whereby the value of a given endogenous variable at one point depends among others on the value of the same variable at adjacent points. This procedure is referred to as an (autoregressive spatial) interpolation because the model yields, among others interpolation 'forecasts' for missing data. To effectuate the estimation, the paper suggests the use of a constrained mathematical program, rather than of a standard statistical routine, as this enables the user to keep a more clear distinction between likelihood function, the autoregressive model, the constraints on parameters and the restrictions implied by the available data.
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تاریخ انتشار 2002