Arbeitspapier

Likelihood inference in some finite mixture models

Parametric mixture models are commonly used in applied work, especially empiri- cal economics, where these models are often employed to learn for example about the proportions of various types in a given population. This paper examines the inference question on the proportions (mixing probability) in a simple mixture model in the pres- ence of nuisance parameters when sample size is large. It is well known that likelihood inference in mixture models is complicated due to 1) lack of point identification, and 2) parameters (for example, mixing probabilities) whose true value may lie on the bound- ary of the parameter space. These issues cause the profiled likelihood ratio (PLR) statistic to admit asymptotic limits that differ discontinuously depending on how the true density of the data approaches the regions of singularities where there is lack of point identification. This lack of uniformity in the asymptotic distribution suggests that confidence intervals based on pointwise asymptotic approximations might lead to faulty inferences. This paper examines this problem in details in a finite mixture model and provides possible fixes based on the parametric bootstrap. We examine the performance of this parametric bootstrap in Monte Carlo experiments and apply it to data from Beauty Contest experiments. We also examine small sample inferences and projection methods.

Sprache
Englisch

Erschienen in
Series: cemmap working paper ; No. CWP19/13

Klassifikation
Wirtschaft
Thema
Finite Mixtures
Parametric Bootstrap
Profiled Likelihood Ratio Statistic
Partial Identification
Parameter on the Boundary

Ereignis
Geistige Schöpfung
(wer)
Chen, Xiaohong
Ponomareva, Maria
Tamer, Elie
Ereignis
Veröffentlichung
(wer)
Centre for Microdata Methods and Practice (cemmap)
(wo)
London
(wann)
2013

DOI
doi:10.1920/wp.cem.2013.1913
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

  • Chen, Xiaohong
  • Ponomareva, Maria
  • Tamer, Elie
  • Centre for Microdata Methods and Practice (cemmap)

Entstanden

  • 2013

Ähnliche Objekte (12)