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Empirical likelihood for spatial dynamic panel data models.

Yinghua Li1, Yongsong Qin1

  • 1College of Mathematics and Statistics, Guangxi Normal University, Guilin, 541004 Guangxi China.

Journal of the Korean Statistical Society
|October 4, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces the empirical likelihood (EL) method for spatial dynamic panel data (SDPD) models. The new EL confidence regions offer superior performance compared to traditional normal approximation methods for SDPD model inference.

Keywords:
Confidence regionEmpirical likelihoodSpatial dynamic panel data modelSpatial error

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Area of Science:

  • Economics
  • Econometrics
  • Statistical Modeling

Background:

  • Spatial dynamic panel data (SDPD) models are increasingly important in economics.
  • Current estimation and testing methods include quasi-maximum likelihood (QML) and generalized method of moments (GMM).

Purpose of the Study:

  • To introduce the empirical likelihood (EL) method for statistical inference in SDPD models.
  • To develop and evaluate EL ratio statistics for parameter estimation and confidence region construction.

Main Methods:

  • Construction of empirical likelihood (EL) ratio statistics for SDPD model parameters.
  • Derivation of the limiting chi-squared distributions for EL ratio statistics.
  • Comparison of EL-based confidence regions with normal approximation methods via simulations.

Main Results:

  • The limiting distributions of the empirical likelihood ratio statistics are shown to be chi-squared distributions.
  • Empirical likelihood based confidence regions demonstrate better performance than normal approximation based confidence regions.
  • EL method provides a viable alternative for statistical inference in SDPD models.

Conclusions:

  • The empirical likelihood method offers a robust approach for statistical inference in spatial dynamic panel data models.
  • EL-based confidence regions provide more accurate results compared to traditional methods.
  • This research expands the toolkit for analyzing complex economic data with spatial and temporal dependencies.