arXiv 27 Aug 2020 · Econometrics · publishedJournal of Econometrics (2021) · 1 citations (OpenAlex)
arXiv:2008.12395 · PDF · DOI · OpenAlex · Extracted main text
Newton-step approximations to pseudo maximum likelihood estimates of spatial autoregressive models with a large number of parameters are examined, in the sense that the parameter space grows slowly as a function of sample size. These have the same asymptotic efficiency properties as maximum likelihood under Gaussianity but are of closed form. Hence they are computationally simple and free from compactness assumptions, thereby avoiding two notorious pitfalls of implicitly defined estimates of large spatial autoregressions. For an initial least squares estimate, the Newton step can also lead to weaker regularity conditions for a central limit theorem than those extant in the literature. A simulation study demonstrates excellent finite sample gains from Newton iterations, especially in large multiparameter models for which grid search is costly. A small empirical illustration shows improvements in estimation precision with real data.
appendix boundary found by none_found · 100% of the source is main text. Read the extracted text to check this.
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 | Gupta, A. and P. M. Robinson (2015) Inference on higher-order spatial autoregressive models with increasingly many parameters self | 1.000 | 18 | 4 | 100% |
| 2 | Gupta, A. and P. M. Robinson (2018) Pseudo maximum likelihood estimation of spatial autoregressive models with increasing dimension self | 1.000 | 14 | 3 | 100% |
| 3 | Lee, L. F (2002) Consistency and efficiency of least squares estimation for mixed regressive, spatial autoregressive models | 0.874 | 10 | 2 | 100% |
| 4 | Lee, L. F (2004) Asymptotic distributions of quasi-maximum likelihood estimators for spatial autoregressive models | 0.811 | 4 | 2 | 100% |
| 5 | Robinson, P. M (1988) The stochastic difference between econometric statistics | 0.737 | 3 | 2 | 100% |
| 6 | Robinson, P. M (2005) Efficiency improvements in inference on stationary and nonstationary fractional time series | 0.644 | 2 | 2 | 100% |
| 7 | Case, A. C (1991) Spatial patterns in household demand | 0.644 | 2 | 2 | 100% |
| 8 | Robinson, P. M (2010) Efficient estimation of the semiparametric spatial autoregressive model | 0.644 | 2 | 2 | 100% |
| 9 | Andrews, D. W. K (1997) A stopping rule for the computation of Generalized Method of Moments estimators | 0.405 | 1 | 1 | 100% |
| 10 | Chudik, A. and M. H. Pesaran (2015) Large panel data models with cross-sectional dependence: A survey | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 51 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Quasi-Score Matching Estimation for Spatial Autoregressive Model with Random Weights Matrix and Regressors | 0.843 | 3 | 3 |