The DOI matches its Crossref record; 73 of the 73 references it cites were found in a registry or an index.
An MCMC approach to classical estimation
Open accessAbstract
This paper studies computationally and theoretically attractive estimators called here Laplace type estimators (LTEs), which include means and quantiles of quasi-posterior distributions defined as transformations of general (nonlikelihood-based) statistical criterion functions, such as those in GMM, nonlinear IV, empirical likelihood, and minimum distance methods. The approach generates an alternative to classical extremum estimation and also falls outside the parametric Bayesian approach. For example, it offers a new attractive estimation method for such important semi-parametric problems as censored and instrumental quantile regression, nonlinear GMM and value-at-risk models. The LTEs are computed using Markov Chain Monte Carlo methods, which help circumvent the computational curse of dimensionality. A large sample theory is obtained and illustrated for regular cases.
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Cites
Numerical recipes in C: the art of scientific computing
(1993) · Choice Reviews Online · doi:10.5860/choice.30-5638 · Cited by 17,933
Newey, W. K., & West, K. D. (1987) · Econometrica · doi:10.2307/1913610 · Cited by 16,936
Large Sample Properties of Generalized Method of Moments Estimators
Hansen, L. P. (1982) · Econometrica · doi:10.2307/1912775 · Cited by 13,467
Koenker, R., & Bassett, G. (1978) · Econometrica · doi:10.2307/1913643 · Cited by 13,316
A Simple, Positive Semi-Definite, Heteroskedasticity and AutocorrelationConsistent Covariance Matrix
Newey, W., & West, K. (1986) · National Bureau of Economic Research · doi:10.3386/t0055 · Cited by 12,278
Numerical recipes in Pascal: the art of scientific computing
(1990) · Choice Reviews Online · doi:10.5860/choice.27-5158 · Cited by 11,811
Jeffreys, H., Moran, P. A. P., & Chatfield, C. (1922) · Nature · doi:10.1093/oso/9780198503682.001.0001 · Cited by 7,878
PE, & Billingsley, P. (1996) · Journal of the American Statistical Association · doi:10.2307/2291440 · Cited by 6,481
Monte Carlo Statistical Methods
Kim, H. (2000) · Technometrics · doi:10.1080/00401706.2000.10485722 · Cited by 5,508
Weak Convergence and Empirical Processes
van der Vaart, A. W., & Wellner, J. A. (1996) · Springer Series in Statistics · doi:10.1007/978-1-4757-2545-2 · Cited by 5,014
Cited by
An IV Model of Quantile Treatment Effects
Chernozhukov, V., & Hansen, C. (2005) · Econometrica · doi:10.1111/j.1468-0262.2005.00570.x · Cited by 988
Self-citationThe Costs of Environmental Regulation in a Concentrated Industry
(2012) · Econometrica · doi:10.3982/ecta6750 · Cited by 668
Instrumental quantile regression inference for structural and treatment effect models
Chernozhukov, V., & Hansen, C. (2006) · Journal of Econometrics · doi:10.1016/j.jeconom.2005.02.009 · Cited by 621
Self-citationGibbs sampling methods for Bayesian quantile regression
Kozumi, H., & Kobayashi, G. (2011) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2010.496117 · Cited by 498
Sarkodie, S. A., & Strezov, V. (2019) · Science of The Total Environment · doi:10.1016/j.scitotenv.2018.11.349 · Cited by 248
Metadata
- Authors
- Chernozhukov, V., & Hong, H.
- Venue
- Journal of Econometrics
- Publisher
- Elsevier BV
- Published
- 2003-08
- Volume
- 115
- Issue
- 2
- Pages
- 293-346
- ISSN
- 0304-4076 (ISSN-L)
- OpenAlex
- W3121290475
- Cited by
- 758
- References
- 73
- FWCI
- 7.82
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
