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
April 2018
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), .
(doi:10.1017/S0266466616000505).
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.
Text
ET3544_RJS8Revision.pdf
- Accepted Manuscript
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
Catalogue record
Date deposited: 13 Jan 2017 15:37
Last modified: 16 Mar 2024 02:42
Export record
Altmetrics
Contributors
Author:
Federico Martellosio
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics