Arbeitspapier
Posterior inference in curved exponential families under increasing dimensions
This work studies the large sample properties of the posteriorbased inference in the curved exponential family under increasing dimension. The curved structure arises from the imposition of various restrictions on the model, such as moment restrictions, and plays a fundamental role in econometrics and others branches of data analysis. We establish conditions under which the posterior distribution is approximately normal, which in turn implies various good properties of estimation and inference procedures based on the posterior. In the process we also revisit and improve upon previous results for the exponential family under increasing dimension by making use of concentration of measure. We also discuss a variety of applications to high-dimensional versions of the classical econometric models including the multinomial model with moment restrictions, seemingly unrelated regression equations, and single structural equation models. In our analysis, both the parameter dimension and the number of moments are increasing with the sample size.
- Language
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Englisch
- Bibliographic citation
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Series: cemmap working paper ; No. CWP68/13
- Classification
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Wirtschaft
- Subject
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curved exponential family
Bernstein-Von Mises theorems
increasing dimension
single-equation structural equations
seemingly unrelated regression
multivariate linear models
multinomial model with moment restrictions
- Event
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Geistige Schöpfung
- (who)
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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)
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2013
- DOI
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doi:10.1920/wp.cem.2013.6813
- Handle
- Last update
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10.03.2025, 11:42 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
- 2013
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Inference for extremal conditional quantile models, with an application to market and birthweight risks
Inference on counterfactual distributions
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Simultaneous inference for best linear predictor of the conditional average treatment effect and other structural functions
Intersection bounds: Estimation and inference
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