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

Preview

Text 1s20S030440761400298Xmain.pdf - Published Version Available under License Creative Commons Attribution. Download (551kB) | Preview

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
Uncontrolled Keywords:	Spatial autoregression; Increasingly many parameters; Central limit theorem; Rate of convergence; Spatial panel data
Subjects:	H Social Sciences > HB Economic Theory
Divisions:	Faculty of Social Sciences Faculty of Social Sciences > Economics, Department of
SWORD Depositor:	Elements
Depositing User:	Elements
Date Deposited:	06 Nov 2018 10:20
Last Modified:	06 Jan 2022 14:51
URI:	http://repository.essex.ac.uk/id/eprint/21580

Available Versions of this Item

• Inference on higher-order spatial autoregressive models with increasingly many parameters. (deposited 20 Jan 2015 11:34)
  • Inference on higher-order spatial autoregressive models with increasingly many parameters. (deposited 06 Nov 2018 10:20) [Currently Displayed]

Actions (login required)

View Item