Assessing Relative Performance of Econometric Models in Measuring the Impact of Climate Change on Agriculture Using Spatial Autoregression

Although econometric models have been widely used to measure the impact of climate change on agriculture, there exist differences among the modelers on which specification should be preferred. To help explain the discrepancies, this paper assesses four different econometric models, i.e., OLS, panel, and two spatial models using a South American agricultural household data. The relationship among the econometric specifications is examined in terms of the freedom given to a spatial autoregressive parameter. In spatial models, the spatial parameter is free within the model, but is fixed a priori in the aspatial models. Empirical results show a high correlation of the land values across South America. Spatial models result in somewhat lower climate change impact estimates than those from the aspatial models.