2020/01/22 by Chaonan Jiang, Jiang, Chaonan, Davide La Vecchia +5 · 4 citations
Decision Sciences · Economics, Econometrics and Finance · #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #FOS: Mathematics #Methodology (stat.ME) #Regional Economics and Spatial Analysis #Spatial and Panel Data Analysis #Statistics Theory (math.ST) #demographic modeling and climate adaptation
paper · pdf · doi:10.48550/arxiv.2001.10377
openalex publication_date 2020/01/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 non-identically distributed setting. The density approximation is always non-negative, 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 expansions. 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 first-order asymptotics and saddlepoint techniques.