A two-step approach to account for unobserved spatial heterogeneity

TL;DR

This paper introduces an algorithmic method to detect structural breaks in spatial data, addressing unobserved heterogeneity in economic models without relying on prior theoretical assumptions.

Contribution

It presents a novel algorithm that endogenously identifies spatial structural breaks, filling a gap in existing spatial econometric methods.

Findings

Successfully applied to house price datasets

Detects spatial heterogeneity without prior assumptions

Enhances understanding of spatial dependence in economic data

Abstract

Empirical analysis in economics often faces the difficulty that the data is correlated and heterogeneous in some unknown form. Spatial parametric approaches have been widely used to account for dependence structures, but the problem of directly deal with spatially varying parameters has been largely unexplored. The problem can be serious in all those cases in which we have no prior information justified by the economic theory. In this paper we propose an algorithm-based procedure which is able to endogenously identify structural breaks in space. The proposed algorithm is illustrated by using two well known house price data sets.