Hodges–Lehmann Estimation of Static Panel Models with Spatially Correlated Disturbances

Several studies point out a substantial downward bias of the Maximum Likelihood (ML) estimator of the spatial correlation parameter under strongly connected spatial structures. This paper proposes Hodges–Lehmann (HL) type interval and point estimators for the spatial parameter in static panel models with spatially autoregressive or moving average disturbances. HL estimators are implemented by means of ‘inverting’ common diagnostics for spatial correlation. Exact inference is implemented by means of Monte Carlo testing. A simulation study covering models with distinct degrees of spatial connectivity shows that HL confidence intervals are characterized by less size distortions and appear more robust against spatial connectivity in comparison with ML interval estimates. In addition, the bias of the HL point estimator based on the Moran’s I statistic is markedly smaller than its ML counterpart.