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GM Estimation of Higher Order Spatial Autoregressive Processes in Panel Data Error Component Models

Author: Harald Badinger; Peter Egger
Publisher: München : CESifo, Center for Economic Studies & Ifo Institute for economic research, 2008.
Series: CESifo working paper series, 2301.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
This 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  Read more...
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Details

Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Harald Badinger; Peter Egger
OCLC Number: 429210765
Description: 1 online resource (Text.)
Series Title: CESifo working paper series, 2301.
Responsibility: Harald Badinger, Peter Egger.

Abstract:

This 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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