Inference of Dynamic Spatial Panel Data Model and Its Application in Carbon Emission Analysis

The Spatial Dynamic Panel Data (SDPD) model is a widely used statistical model in the fields of economics and social sciences and has been the subject of extensive research by many scholars in recent years. Existing methods for parameter estimation primarily focus on improvements to the Generalized...

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Bibliographic Details
Published inJournal of physics. Conference series Vol. 2747; no. 1; pp. 12028 - 12038
Main Authors Gu, Wenbo, Kang, Fangyuan
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.05.2024
Subjects
Asymptotic methods
Carbon
Carbon emissions
Confidence regions
Dynamic spatial panel data model
Emission analysis
Emissions
Empirical analysis
Empirical likelihood inference
Method of moments
Parameter estimation
Spatial data
Statistical inference
Statistical models
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Summary:The Spatial Dynamic Panel Data (SDPD) model is a widely used statistical model in the fields of economics and social sciences and has been the subject of extensive research by many scholars in recent years. Existing methods for parameter estimation primarily focus on improvements to the Generalized Method of Moments (GMM) and Quasi-Maximum Likelihood (QML). In this paper, we employ the method of Empirical Likelihood (EL) for statistical inference of the dynamic spatial panel data model and obtain confidence regions for the parameters. Through numerical simulations, we present the performance of the confidence regions obtained using the Empirical Likelihood (EL) and the Asymptotically Normal (NA) methods under finite samples and compare the two approaches. Finally, we analyze carbon using the suggested model and techniques.
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ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2747/1/012028