Continuously Updated Indirect Inference in Heteroskedastic Spatial Models

Abstract

Spatial units typically vary over many of their characteristics, introducing potential unobserved heterogeneity which invalidates commonly used homoskedasticity conditions. In the presence of unobserved heteroskedasticity, standard methods based on the (quasi-)likelihood function generally produce inconsistent estimates of both the spatial parameter and the coefficients of the exogenous regressors. A robust generalized method of moments estimator as well as a modified likelihood method have been proposed in the literature to address this issue. The present paper constructs an alternative indirect inference approach which relies on a simple ordinary least squares procedure as its starting point. Heteroskedasticity is accommodated by utilizing a new version of continuous updating that is applied within the indirect inference procedure to take account of the parametrization of the variance-covariance matrix of the disturbances. Finite sample performance of the new estimator is assessed in a Monte Carlo study and found to offer advantages over existing methods. The approach is implemented in an empirical application to house price data in the Boston area, where it is found that spatial effects in house price determination are much more significant under robustification to heterogeneity in the equation errors.

Raising the bar (18)

“…Initially, Kyriacou et al (2017) started to consider this estimator for the spatial autoregressive (SAR) model. Then, Kyriacou et al (2019) and Bao et al (2020) extended this spatial econometric model with exogenous regressors and heteroskedastic errors. The paper by Bao and Liu (2021, this issue) further extends the latter model by also considering a spatial lag in the error term specification.…”