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

Abhimanyu Gupta and Peter M. Robinson

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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

Keywords: spatial autoregression; increasingly many parameters; central limit theorem; rate of convergence; spatial panel data (search for similar items in EconPapers)
JEL-codes: C21 C31 C33 (search for similar items in EconPapers)
Date: 2015-05
New Economics Papers: this item is included in nep-geo and nep-ure
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Citations: View citations in EconPapers (40)

Published in Journal of Econometrics, May, 2015, 186(1), pp. 19-31. ISSN: 0304-4076

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http://eprints.lse.ac.uk/60794/ Open access version. (application/pdf)

Related works:
Journal Article: Inference on higher-order spatial autoregressive models with increasingly many parameters (2015)
Working Paper: Inference on Higher-Order Spatial Autoregressive Models with Increasingly Many Parameters (2013)
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