GM Estimation of Higher Order Spatial Autoregressive Processes in Panel Data Error Component Models
AbstractThis paper presents a generalized moments (GM) approach to estimating an R-th order spatial regressive process in a panel data error component model. We derive moment conditions to estimate the parameters of the higher order spatial regressive process and the optimal weighting matrix required to achieve asymptotic efficiency. We prove consistency of the proposed GM estimator and provide Monte Carlo evidence that it performs well also in reasonably small samples.
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Bibliographic InfoPaper provided by CESifo Group Munich in its series CESifo Working Paper Series with number 2301.
Date of creation: 2008
Date of revision:
spatial models; panel data models; error component models;
Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
- C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Longitudinal Data; Spatial Time Series
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