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Cited by 723

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Maximum likelihood estimation of limited and discrete dependent variable models with nested random effects

Open access
Rabe-Hesketh, Sophia, Skrondal, Anders, Pickles, Andrew·2005·Journal of Econometrics
DOI 10.1016/j.jeconom.2004.08.017
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Abstract

Abstract Gauss–Hermite quadrature is often used to evaluate and maximize the likelihood for random component probit models. Unfortunately, the estimates are biased for large cluster sizes and/or intraclass correlations. We show that adaptive quadrature largely overcomes these problems. We then extend the adaptive quadrature approach to general random coefficient models with limited and discrete dependent variables. The models can include several nested random effects (intercepts and coefficients) representing unobserved heterogeneity at different levels of a hierarchical dataset. The required multivariate integrals are evaluated efficiently using spherical quadrature rules. Simulations show that adaptive quadrature performs well in a wide range of situations.

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Metadata

Authors
Rabe-Hesketh, S., Skrondal, A., & Pickles, A.
Venue
Journal of Econometrics
Publisher
Elsevier BV
Published
2005-10
Volume
128
Issue
2
Pages
301-323
ISSN
0304-4076 (ISSN-L)
DOI
10.1016/j.jeconom.2004.08.017
OpenAlex
W2046376969
Cited by
723
References
3
FWCI
23.29
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).

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