Scientific article
Open access

Saddlepoint Approximations for Spatial Panel Data Models

Published inJournal of the American Statistical Association, vol. 118, no. 542, p. 1164-1175
Publication date2021-11-17
First online date2021-11-17

We develop new higher-order asymptotic techniques for the Gaussian maximum likelihood estimator in

a spatial panel data model, with fixed effects, time-varying covariates, and spatially correlated errors. Our

saddlepoint density and tail area approximation feature relative error of order O(1/(n(T − 1))) with n

being the cross-sectional dimension and T the time-series dimension. The main theoretical tool is the

tilted-Edgeworth technique in a nonidentically distributed setting. The density approximation is always

nonnegative, does not need resampling, and is accurate in the tails. Monte Carlo experiments on density

approximation and testing in the presence of nuisance parameters illustrate the good performance of

our approximation over first-order asymptotics and Edgeworth expansion. An empirical application to

the investment–saving relationship in OECD (Organisation for Economic Co-operation and Development)

countries shows disagreement between testing results based on the first-order asymptotics and saddlepoint

techniques. Supplementary materials for this article, including a standardized description of the materials

available for reproducing the work, are available as an online supplement.

  • Higher-order asymptotics
  • Investment-saving
  • Random field
  • Tail area
Citation (ISO format)
JIANG, Chaonan et al. Saddlepoint Approximations for Spatial Panel Data Models. In: Journal of the American Statistical Association, 2021, vol. 118, n° 542, p. 1164–1175. doi: 10.1080/01621459.2021.1981913
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Article (Published version)
Article (Submitted version)
ISSN of the journal0162-1459

Technical informations

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