Hierarchically spatial autoregressive and moving average error model

Qianting Ye, Huajie Liang, Kuan-Pin Lin and Zhihe Long

Economic Modelling, 2019, vol. 76, issue C, 14-30

Abstract: This paper considers a hierarchically spatial autoregressive and moving average error (HSEARMA) model. This model captures the spatially autoregressive and moving average error correlation, the county-level random effects, and the district-level random effects nested within each county. We propose optimal generalized method of moments (GMM) estimators for the spatial error correlation coefficient and the error components' variances terms, as well as a feasible generalized least squares (FGLS) estimator for the regression parameter vector. Further, we prove consistency of the GMM estimator and establish the asymptotic distribution of the FGLS estimator. A finite-scale Monte Carlo simulation is conducted to demonstrate the good finite sample performances of our GMM-FGLS estimators.

Keywords: Hierarchically spatial autoregressive and moving average error model; Hierarchical data structure; GMM-FGLS estimation; Monte Carlo simulation (search for similar items in EconPapers)
JEL-codes: C13 C23 C33 (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:76:y:2019:i:c:p:14-30

DOI: 10.1016/j.econmod.2018.06.022

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