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Continuously updated indirect inference in heteroskedastic spatial models

Continuously updated indirect inference in heteroskedastic spatial models
Continuously updated indirect inference in heteroskedastic spatial models
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.
Spatial autoregression;, Unknown heteroskedasticity, Indirect inference, Robust methods, Weights matrix
Cowles Foundation Discussion Paper
Kyriacou, Maria
6234587e-81f1-4e1d-941d-395996f8bda7
Phillips, Peter Charles Bonest
f67573a4-fc30-484c-ad74-4bbc797d7243
Rossi, Francesca
1cdd87b3-bc01-40b0-ad91-0db0ee24e8e0
Kyriacou, Maria
6234587e-81f1-4e1d-941d-395996f8bda7
Phillips, Peter Charles Bonest
f67573a4-fc30-484c-ad74-4bbc797d7243
Rossi, Francesca
1cdd87b3-bc01-40b0-ad91-0db0ee24e8e0

Kyriacou, Maria, Phillips, Peter Charles Bonest and Rossi, Francesca (2019) Continuously updated indirect inference in heteroskedastic spatial models Yale University, USA. Cowles Foundation Discussion Paper 41pp.

Record type: Monograph (Discussion Paper)

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.

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More information

Published date: 2019
Keywords: Spatial autoregression;, Unknown heteroskedasticity, Indirect inference, Robust methods, Weights matrix

Identifiers

Local EPrints ID: 436591
URI: http://eprints.soton.ac.uk/id/eprint/436591
PURE UUID: 3b7389b9-aef9-4c1a-bd20-d556d74a6f0b
ORCID for Peter Charles Bonest Phillips: ORCID iD orcid.org/0000-0003-2341-0451

Catalogue record

Date deposited: 17 Dec 2019 17:30
Last modified: 18 Dec 2019 01:33

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