Drift and Diffusion in Geospatial Econometrics: Implications for Panel Data and Time Series

Economic data is highly dependent on its arrangement within space and time. Perhaps the most obvious and important definition of space is geospatial configuration on the Earth’s surface. Consideration of geospatial effects produces a dramatic improvement in the prediction of median housing prices across 20,640 districts in California. Unconditional regression with engineered variables, two-stage least squares regression (2SLS), and iterative local regression approach r2 ≈ 0.8536, the goodness of fit attained in the original California study. Geospatial methods can be generalized to panel data analysis and time-series forecasting. Distance-sensitive analysis reveals the value of treating time-variant data as potentially discrete and discontinuous. This insight highlights the value of methodologies that suspend the assumption that data varies in a continuous or even linear fashion across space and time.