Inference on higher-order spatial autoregressive models with increasingly many parameters

Gupta, Abhimanyu and Robinson, Peter M. (2015) Inference on higher-order spatial autoregressive models with increasingly many parameters. Journal of Econometrics, 186 (1). pp. 19-31. ISSN 0304-4076

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Abstract

This paper develops consistency and asymptotic normality of parameter estimates for a higher-order spatial autoregressive model whose order, and number of regressors, are allowed to approach infinity slowly with sample size. Both least squares and instrumental variables estimates are examined, and the permissible rate of growth of the dimension of the parameter space relative to sample size is studied. Besides allowing the number of parameters to increase with the data, this has the advantage of accommodating some asymptotic regimes that are suggested by certain spatial settings, several of which are discussed. A small empirical example is also included, and a Monte Carlo study analyses various implications of the theory in finite samples

Item Type:	Article
Official URL:	http://www.sciencedirect.com/science/journal/03044...
Additional Information:	© 2015 The Authors © CC BY 4.0
Divisions:	Economics
Subjects:	H Social Sciences > HB Economic Theory
JEL classification:	C - Mathematical and Quantitative Methods > C2 - Econometric Methods: Single Equation Models; Single Variables > C21 - Cross-Sectional Models; Spatial Models; Treatment Effect Models C - Mathematical and Quantitative Methods > C3 - Econometric Methods: Multiple; Simultaneous Equation Models; Multiple Variables; Endogenous Regressors > C31 - Cross-Sectional Models; Spatial Models; Treatment Effect Models C - Mathematical and Quantitative Methods > C3 - Econometric Methods: Multiple; Simultaneous Equation Models; Multiple Variables; Endogenous Regressors > C33 - Models with Panel Data
Date Deposited:	28 Jan 2015 09:28
Last Modified:	12 Dec 2024 00:50
Projects:	ES/J007242/1
Funders:	Economic and Social Research Council
URI:	http://eprints.lse.ac.uk/id/eprint/60794

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