Arbeitspapier
On the computational complexity of MCMC-based estimators in large samples
In this paper we examine the implications of the statistical large sample theory for the computational complexity of Bayesian and quasi-Bayesian estimation carried out using Metropolis random walks. Our analysis is motivated by the Laplace-Bernstein-Von Mises central limit theorem, which states that in large samples the posterior or quasi-posterior approaches a normal density. Using this observation, we establish polynomial bounds on the computational complexity of general Metropolis random walks methods in large samples. Our analysis covers cases, where the underlying log-likelihood or extremum criterion function is possibly nonconcave, discontinuous, and of increasing dimension. However, the central limit theorem restricts the deviations from continuity and log-concavity of the log-likelihood or extremum criterion function in a very specific manner.
- Language
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Englisch
- Bibliographic citation
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Series: cemmap working paper ; No. CWP12/07
- Classification
-
Wirtschaft
- Subject
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Monte Carlo , Computational Complexity , Curved Exponential
Markovscher Prozess
Monte-Carlo-Methode
Mathematik
- Event
-
Geistige Schöpfung
- (who)
-
Belloni, Alexandre
Chernozhukov, Victor
- Event
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Veröffentlichung
- (who)
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Centre for Microdata Methods and Practice (cemmap)
- (where)
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London
- (when)
-
2007
- DOI
-
doi:10.1920/wp.cem.2007.1207
- Handle
- Last update
-
10.03.2025, 11:44 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Belloni, Alexandre
- Chernozhukov, Victor
- Centre for Microdata Methods and Practice (cemmap)
Time of origin
- 2007
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Comparison and anti-concentration bounds for maxima of Gaussian random vectors
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Quantile models with endogeneity
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