arXiv 6 May 2022 · Econometrics · publishedJournal of Econometrics (2023) · 3 citations (OpenAlex)
arXiv:2205.03254 · PDF · DOI · OpenAlex · Extracted main text
In non-linear estimations, it is common to assess sampling uncertainty by bootstrap inference. For complex models, this can be computationally intensive. This paper combines optimization with resampling: turning stochastic optimization into a fast resampling device. Two methods are introduced: a resampled Newton-Raphson (rNR) and a resampled quasi-Newton (rqN) algorithm. Both produce draws that can be used to compute consistent estimates, confidence intervals, and standard errors in a single run. The draws are generated by a gradient and Hessian (or an approximation) computed from batches of data that are resampled at each iteration. The proposed methods transition quickly from optimization to resampling when the objective is smooth and strictly convex. Simulated and empirical applications illustrate the properties of the methods on large scale and computationally intensive problems. Comparisons with frequentist and Bayesian methods highlight the features of the algorithms.
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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 | Donaldson, D (2018) Railroads of the Raj: Estimating the impact of transportation infrastructure | 0.874 | 5 | 2 | 100% |
| 2 | Forneron, J.-J. and S. Ng (2021) Estimation and Inference by Stochastic Optimization: Three Examples, in self | 0.843 | 3 | 3 | 100% |
| 3 | Davidson, R. and J. G. MacKinnon (1999) Bootstrap Testing in Nonlinear Models | 0.737 | 3 | 2 | 100% |
| 4 | Kline, P. and A. Santos (2012) A Score Based Approach to Wild Bootstrap Inference | 0.737 | 3 | 2 | 100% |
| 5 | Newey, W. and D. McFadden (1994) Large Sample Estimation and Hypothesis Testing, in | 0.737 | 3 | 2 | 100% |
| 6 | van der Vaart, A. W. and J. A. Wellner (1996) Weak Convergence and Empirical Processes | 0.693 | 6 | 3 | 33% |
| 7 | Nocedal, J. and S. Wright (2006) Numerical Optimization | 0.644 | 4 | 2 | 50% |
| 8 | Andrews, D. W. K (2002) Higher-Order Improvements of a Computationally Attractive k-Step Bootstrap for Extremum Estimators | 0.644 | 2 | 2 | 100% |
| 9 | Forneron, J.-J (2022) Noisy, Non-Smooth, Non-Convex Estimation of Moment Condition Models self | 0.644 | 2 | 2 | 100% |
| 10 | Honoré, B. E. and L. Hu (2017) Poor (Wo)man's Bootstrap | 0.644 | 2 | 2 | 100% |
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arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.