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Markov Chain Monte Carlo estimation of spatial dynamic panel models for large samples

James P. LeSage, Yao-Yu Chih and Colin Vance

Computational Statistics & Data Analysis, 2019, vol. 138, issue C, 107-125

Abstract: Focus is on efficient estimation of a dynamic space–time panel data model that incorporates spatial dependence, temporal dependence, as well as space–time covariance and can be implemented where there are a large number of spatial units and time periods. Quasi-maximum likelihood (QML) estimation in cases involving large samples poses computational challenges because optimizing the (log) likelihood requires: (1) evaluating the log-determinant of a large matrix that appears in the likelihood, (2) imposing stability restrictions on parameters reflecting space–time dynamics, and (3) simulations to produce an empirical distribution of the partial derivatives used to interpret model estimates that require numerous inversions of large matrices.

Keywords: Spatial; Time dependence; Dynamic panels; Log-marginal likelihood (search for similar items in EconPapers)
Date: 2019
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