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Abstract
Controlling for unobserved heterogeneity is a fundamental challenge in
empirical research, as failing to do so can introduce omitted variables biases and
preclude causal inference. In this paper we develop an innovative method – the
Iterative Geographically Weighted Regression (IGWR) method – to identify clusters
of farms that follow a similar local production econometric model, taking explicitly
unobserved spatial heterogeneity into account. The proposed method is the perfect
combination of the GWR approach and the adaptive weights smoothing (AWS)
procedure. This method is applied to regional samples of olive growing farms in Italy.
The main finding is that the conditional global IGWR model fits the data best, proving
that explicitly accounting for unobserved spatial heterogeneity is of crucial
importance when modeling the production function of firms particularly for those
operating in land based industries