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, 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 (II) 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 II procedure to take account of the parameterization of the variance-covariance matrix of the disturbances. Finite-sample performance of the new estimator is assessed in a Monte Carlo study. 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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Kyriacou, Maria
6234587e-81f1-4e1d-941d-395996f8bda7
Phillips, Peter Charles Bonest
f67573a4-fc30-484c-ad74-4bbc797d7243
Rossi, Francesca
1cdd87b3-bc01-40b0-ad91-0db0ee24e8e0
22 September 2021
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
(2021)
Continuously updated indirect inference in heteroskedastic spatial models.
Econometric Theory, .
(doi:10.1017/S0266466621000384).
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, 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 (II) 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 II procedure to take account of the parameterization of the variance-covariance matrix of the disturbances. Finite-sample performance of the new estimator is assessed in a Monte Carlo study. 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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Accepted/In Press date: 14 April 2021
Published date: 22 September 2021
Identifiers
Local EPrints ID: 448881
URI: http://eprints.soton.ac.uk/id/eprint/448881
ISSN: 0266-4666
PURE UUID: a66aafc8-8596-40a6-a6b3-92b3bff1bf51
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Date deposited: 07 May 2021 16:34
Last modified: 16 Mar 2024 12:04
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Author:
Francesca Rossi
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