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.

Sprache
Englisch

Erschienen in
Series: cemmap working paper ; No. CWP68/13

Klassifikation
Wirtschaft
Thema
curved exponential family
Bernstein-Von Mises theorems
increasing dimension
single-equation structural equations
seemingly unrelated regression
multivariate linear models
multinomial model with moment restrictions

Ereignis
Geistige Schöpfung
(wer)
Belloni, Alexandre
Chernozhukov, Victor
Ereignis
Veröffentlichung
(wer)
Centre for Microdata Methods and Practice (cemmap)
(wo)
London
(wann)
2013

DOI
doi:10.1920/wp.cem.2013.6813
Handle
Letzte Aktualisierung
10.03.2025, 11:42 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Belloni, Alexandre
  • Chernozhukov, Victor
  • Centre for Microdata Methods and Practice (cemmap)

Entstanden

  • 2013

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