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

Abhimanyu Gupta and Peter M. Robinson

Journal of Econometrics, 2015, vol. 186, issue 1, 19-31

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 C36 (search for similar items in EconPapers)
Date: 2015
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Working Paper: Inference on higher-order spatial autoregressive models with increasingly many parameters (2015) Downloads
Working Paper: Inference on Higher-Order Spatial Autoregressive Models with Increasingly Many Parameters (2013) Downloads
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DOI: 10.1016/j.jeconom.2014.12.008

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Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

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