Delgado, Miguel A. and Robinson, Peter (2015) Non-nested testing of spatial correlation. Journal of Econometrics, 187 (1). pp. 385-401. ISSN 0304-4076
|
PDF
- Published Version
Available under License Creative Commons Attribution. Download (960kB) | Preview |
Abstract
We develop non-nested tests in a general spatial, spatio-temporal or panel data context. The spatial aspect can be interpreted quite generally, in either a geographical sense, or employing notions of economic distance, or when parametric modelling arises in part from a common factor or other structure. In the former case, observations may be regularly-spaced across one or more dimensions, as is typical with much spatio-temporal data, or irregularly-spaced across all dimensions; both isotropic models and non-isotropic models can be considered, and a wide variety of correlation structures. In the second case, models involving spatial weight matrices are covered, such as “spatial autoregressive models”. The setting is sufficiently general to potentially cover other parametric structures such as certain factor models, and vector-valued observations, and here our preliminary asymptotic theory for parameter estimates is of some independent value. The test statistic is based on a Gaussian pseudo-likelihood ratio, and is shown to have an asymptotic standard normal distribution under the null hypothesis that one of the two models is correct; this limit theory rests strongly on a central limit theorem for the Gaussian pseudo-maximum likelihood parameter estimates. A small Monte Carlo study of finite-sample performance is included.
| Item Type: | Article |
|---|---|
| Official URL: | http://www.sciencedirect.com/science/journal/03044... |
| Additional Information: | © 2015 The Authors |
| Divisions: | Economics |
| Subjects: | H Social Sciences > HB Economic Theory |
| JEL classification: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C12 - Hypothesis Testing C - Mathematical and Quantitative Methods > C2 - Econometric Methods: Single Equation Models; Single Variables > C21 - Cross-Sectional Models; Spatial Models; Treatment Effect Models |
| Date Deposited: | 01 Apr 2015 09:40 |
| Last Modified: | 07 Nov 2024 06:18 |
| Projects: | ES/J007242/1 |
| Funders: | Economic and Social Research Council |
| URI: | http://eprints.lse.ac.uk/id/eprint/61433 |
Actions (login required)
 |
View Item |
Download Statistics
Download Statistics