Jean-Jacques Forneron, Serena Ng
arXiv 6 Jan 2015 · Statistics — Methodology · publishedJournal of Econometrics (2018) · 8 citations (OpenAlex)
arXiv:1501.01265 · PDF · DOI · OpenAlex · Extracted main text
The frequentist method of simulated minimum distance (SMD) is widely used in economics to estimate complex models with an intractable likelihood. In other disciplines, a Bayesian approach known as Approximate Bayesian Computation (ABC) is far more popular. This paper connects these two seemingly related approaches to likelihood-free estimation by means of a Reverse Sampler that uses both optimization and importance weighting to target the posterior distribution. Its hybrid features enable us to analyze an ABC estimate from the perspective of SMD. We show that an ideal ABC estimate can be obtained as a weighted average of a sequence of SMD modes, each being the minimizer of the deviations between the data and the model. This contrasts with the SMD, which is the mode of the average deviations. Using stochastic expansions, we provide a general characterization of frequentist estimators and those based on Bayesian computations including Laplace-type estimators. Their differences are illustrated using analytical examples and a simulation study of the dynamic panel model.
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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 | Creel and Kristensen (2013) Indirect Likelihood Inference, mimeo, UCL | 1.000 | 6 | 4 | 100% |
| 2 | Forneron and Ng (2016) A Likelihood Free Reverse Sampler of the Posterior Distribution, in G. Gonzalez-Rivera, R. C | 1.000 | 5 | 4 | 100% |
| 3 | Gao and Hong (2014) A Computational Implementation of GMM, SSRN Working Paper 2503199 | 0.928 | 4 | 4 | 100% |
| 4 | Beaumont, Zhang and Balding (2002) Approximate Bayesian Computation in Population Genetics, Genetics 162, 2025–2035 | 0.843 | 3 | 3 | 100% |
| 5 | Chernozhukov and Hong (2003) An MCMC Approach to Classical Estimation, Journal of Econometrics 115:2, 293–346 | 0.811 | 4 | 2 | 100% |
| 6 | Creel, Gao, Hong and Kristensen (2016) Bayesian Indirect Inference and the ABC of GMM, unpublished manuscript | 0.737 | 3 | 2 | 100% |
| 7 | Gallant and Tauchen (1996) Which Moments to Match, Econometric Theory 12, 657–681 | 0.737 | 3 | 2 | 100% |
| 8 | Gouriéroux, Monfort and Renault (1993) Indirect Inference, Journal of Applied Econometrics 85, 85–118 | 0.737 | 3 | 2 | 100% |
| 9 | Gouriéroux, Phillips and Yu (2010) Indirect Inference of Dynamic Panel Models, Journal of Econometrics 157(1), 68–77 | 0.737 | 3 | 2 | 100% |
| 10 | Jiang and Turnbull (2004) The Indirect Method: Inference Based on Intermediate Statistics- A Synthesis and Examples, Statistical Science 19(2), 239–263 | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 49 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Generalized Laplace Inference in Multiple Change-Points Models | 0.405 | 1 | 1 |
| 2 | Continuous Record Laplace-based Inference about the Break Date in Structural Change Models | 0.405 | 1 | 1 |
| 3 | Inference by Stochastic Optimization: A Free-Lunch Bootstrap | 0.405 | 1 | 1 |
| 4 | Spatial Econometrics for Misaligned Data | 0.405 | 1 | 1 |