Chaonan Jiang, Davide La Vecchia, Elvezio Ronchetti, Olivier Scaillet
arXiv 22 Jan 2020 · Mathematics — Statistics Theory · publishedJournal of the American Statistical Association (2021) · 7 citations (OpenAlex)
arXiv:2001.10377 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Lee, L. and Yu, J (2010) Estimation of Spatial Autoregressive Panel Data Models With Fixed Effects, Journal of Econometrics, 154, 165–185 | 1.000 | 14 | 4 | 100% |
| 2 | Robinson, P. M. and Rossi, F (2015) Refinements in Maximum Likelihood Inference on Spatial Autocorrelation in Panel Data, Journal of Econometrics, 189, 447–456 | 1.000 | 8 | 3 | 100% |
| 3 | Martellosio, F. and Hillier, G (2020) Adjusted QMLE for the Spatial Autoregressive Parameter, Journal of Econometrics, 219, 488–506 | 0.928 | 4 | 3 | 100% |
| 4 | Hillier, G. and Martellosio, F (2018) Exact and Higher-order Properties of the MLE in Spatial Autoregressive Models, With Applications to Inference, Journal of Econom… | 0.843 | 3 | 3 | 100% |
| 5 | Field, C. A. and Ronchetti, E (1990) Small Sample Asymptotics, vl. 13, IMS, Lecture notes-monograph series self | 0.737 | 3 | 2 | 100% |
| 6 | Lee, L (2004) Asymptotic Distributions of Quasi-maximum Likelihood Estimators for Spatial Autoregressive Models, Econometrica, 72, 1899–1925 | 0.737 | 3 | 2 | 100% |
| 7 | Robinson, P. M. and Rossi, F (2014) Refined Tests for Spatial Correlation, Econometric Theory, 31, 1–32 | 0.737 | 3 | 2 | 100% |
| 8 | Bickel, P., Götze, F., and Van Zwet, W (1986) The Edgeworth Expansion for U-statistics of Degree Two, The Annals of Statistics, 14, 1463–1484 | 0.693 | 5 | 1 | 100% |
| 9 | Debarsy, N. and Ertur, C (2010) Testing for Spatial Autocorrelation in a Fixed Effects Panel Data Model, Regional Science and Urban Economics, 40, 453–470 | 0.644 | 4 | 1 | 100% |
| 10 | Baltagi, B (2008) Econometric Analysis of Panel Data, vl. 1, Wiley, New York | 0.644 | 2 | 2 | 100% |
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