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Indirect inference in spatial autoregression

Indirect inference in spatial autoregression
Indirect inference in spatial autoregression
Ordinary least squares (OLS) is well known to produce an inconsistent estimator of the spatial parameter in pure spatial autoregression (SAR). This paper Ordinary least squares (OLS) is well known to produce an inconsistent estimator of the spatial parameter in pure spatial autoregression (SAR). This paper explores the potential of indirect inference to correct the inconsistency of OLS. Under broad conditions, it is shown that indirect inference (II) based on OLS produces consistent and asymptotically normal estimates in pure SAR regression. The II estimator used here is robust to departures from normal disturbances and is computationally straightforward compared with quasi maximum likelihood (QML). Monte Carlo experiments based on various specifications of the weight matrix show that: (a) the indirect inference estimator displays little bias even in very small samples and gives overall performance that is comparable to the QML while raising variance in some cases; (b) indirect inference applied to QML also enjoys good finite sample properties; and (c) indirect inference shows robust performance in the presence of heavy tailed error distributions.
168-189
Kyriacou, Maria
6234587e-81f1-4e1d-941d-395996f8bda7
Phillips, Peter C.B.
f67573a4-fc30-484c-ad74-4bbc797d7243
Rossi, Francesca
1cdd87b3-bc01-40b0-ad91-0db0ee24e8e0
Kyriacou, Maria
6234587e-81f1-4e1d-941d-395996f8bda7
Phillips, Peter C.B.
f67573a4-fc30-484c-ad74-4bbc797d7243
Rossi, Francesca
1cdd87b3-bc01-40b0-ad91-0db0ee24e8e0

Kyriacou, Maria, Phillips, Peter C.B. and Rossi, Francesca (2017) Indirect inference in spatial autoregression. The Econometrics Journal, 20 (2), 168-189. (doi:10.1111/ectj.12084).

Record type: Article

Abstract

Ordinary least squares (OLS) is well known to produce an inconsistent estimator of the spatial parameter in pure spatial autoregression (SAR). This paper Ordinary least squares (OLS) is well known to produce an inconsistent estimator of the spatial parameter in pure spatial autoregression (SAR). This paper explores the potential of indirect inference to correct the inconsistency of OLS. Under broad conditions, it is shown that indirect inference (II) based on OLS produces consistent and asymptotically normal estimates in pure SAR regression. The II estimator used here is robust to departures from normal disturbances and is computationally straightforward compared with quasi maximum likelihood (QML). Monte Carlo experiments based on various specifications of the weight matrix show that: (a) the indirect inference estimator displays little bias even in very small samples and gives overall performance that is comparable to the QML while raising variance in some cases; (b) indirect inference applied to QML also enjoys good finite sample properties; and (c) indirect inference shows robust performance in the presence of heavy tailed error distributions.

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ectj12084 - Accepted Manuscript
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Accepted/In Press date: 8 February 2017
e-pub ahead of print date: 11 February 2017
Published date: June 2017
Organisations: Economics

Identifiers

Local EPrints ID: 405721
URI: http://eprints.soton.ac.uk/id/eprint/405721
PURE UUID: 3c0f8297-cd93-495b-abee-97f22e317d90
ORCID for Maria Kyriacou: ORCID iD orcid.org/0000-0001-7996-2015
ORCID for Peter C.B. Phillips: ORCID iD orcid.org/0000-0003-2341-0451

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Date deposited: 18 Feb 2017 00:22
Last modified: 16 Mar 2024 05:01

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Contributors

Author: Maria Kyriacou ORCID iD
Author: Francesca Rossi

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