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Exact likelihood inference in group interaction network models

Exact likelihood inference in group interaction network models
Exact likelihood inference in group interaction network models
The paper studies spatial autoregressive models with group interaction structure, focussing on estimation and inference for the spatial autoregressive parameter λ. The quasi-maximum likelihood estimator for λ usually cannot be written in closed form, but using an exact result obtained earlier by the authors for its distribution function, we are able to provide a complete analysis of the properties of the estimator, and exact inference that can be based on it, in models that are balanced. This is presented first for the so-called pure model, with no regression component, but is also extended to some special cases of the more general model. We then study the much more difficult case of unbalanced models, giving analogues of some, but by no means all, of the results obtained for the balanced case earlier. In both balanced and unbalanced models, results obtained for the pure model generalize immediately to the model with group-specific regression components.
383-415
Hillier, Grant
3423bd61-c35f-497e-87a3-6a5fca73a2a1
Martellosio, Federico
4fa40068-a4be-4f23-be6f-83cbdc33685b
Hillier, Grant
3423bd61-c35f-497e-87a3-6a5fca73a2a1
Martellosio, Federico
4fa40068-a4be-4f23-be6f-83cbdc33685b

Hillier, Grant and Martellosio, Federico (2018) Exact likelihood inference in group interaction network models. Econometric Theory, 34 (2), 383-415. (doi:10.1017/S0266466616000505).

Record type: Article

Abstract

The paper studies spatial autoregressive models with group interaction structure, focussing on estimation and inference for the spatial autoregressive parameter λ. The quasi-maximum likelihood estimator for λ usually cannot be written in closed form, but using an exact result obtained earlier by the authors for its distribution function, we are able to provide a complete analysis of the properties of the estimator, and exact inference that can be based on it, in models that are balanced. This is presented first for the so-called pure model, with no regression component, but is also extended to some special cases of the more general model. We then study the much more difficult case of unbalanced models, giving analogues of some, but by no means all, of the results obtained for the balanced case earlier. In both balanced and unbalanced models, results obtained for the pure model generalize immediately to the model with group-specific regression components.

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ET3544_RJS8Revision.pdf - Accepted Manuscript
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More information

Accepted/In Press date: 5 November 2016
e-pub ahead of print date: 19 December 2016
Published date: April 2018
Organisations: Economics

Identifiers

Local EPrints ID: 404637
URI: http://eprints.soton.ac.uk/id/eprint/404637
PURE UUID: 5445190a-ff96-40e8-9433-670c993abf89
ORCID for Grant Hillier: ORCID iD orcid.org/0000-0003-3261-5766

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Date deposited: 13 Jan 2017 15:37
Last modified: 16 Mar 2024 02:42

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Contributors

Author: Grant Hillier ORCID iD
Author: Federico Martellosio

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