Details
| Original language | English |
|---|---|
| Title of host publication | Modern Econometric Analysis |
| Subtitle of host publication | Surveys on Recent Developments |
| Pages | 119-135 |
| Number of pages | 17 |
| Publication status | Published - 2006 |
Abstract
This paper presents a selective survey on panel data methods. The focus is on new developments. In particular, linear multilevel models, specific nonlinear, nonparametric and semiparametric models are at the center of the survey. In contrast to linear models there do not exist unified methods for nonlinear approaches. In this case conditional maximum likelihood methods dominate for fixed effects models. Under random effects assumptions it is sometimes possible to employ conventional maximum likelihood methods using Gaussian quadrature to reduce a T-dimensional integral. Alternatives are generalized methods of moments and simulated estimators. If the nonlinear function is not exactly known, nonparametric or semiparametric methods should be preferred.
ASJC Scopus subject areas
- Economics, Econometrics and Finance(all)
- Business, Management and Accounting(all)
- General Business,Management and Accounting
Cite this
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Modern Econometric Analysis: Surveys on Recent Developments. 2006. p. 119-135.
Research output: Chapter in book/report/conference proceeding › Contribution to book/anthology › Research › peer review
}
TY - CHAP
T1 - Multilevel and Nonlinear Panel Data Models
AU - Hübler, Olaf
PY - 2006
Y1 - 2006
N2 - This paper presents a selective survey on panel data methods. The focus is on new developments. In particular, linear multilevel models, specific nonlinear, nonparametric and semiparametric models are at the center of the survey. In contrast to linear models there do not exist unified methods for nonlinear approaches. In this case conditional maximum likelihood methods dominate for fixed effects models. Under random effects assumptions it is sometimes possible to employ conventional maximum likelihood methods using Gaussian quadrature to reduce a T-dimensional integral. Alternatives are generalized methods of moments and simulated estimators. If the nonlinear function is not exactly known, nonparametric or semiparametric methods should be preferred.
AB - This paper presents a selective survey on panel data methods. The focus is on new developments. In particular, linear multilevel models, specific nonlinear, nonparametric and semiparametric models are at the center of the survey. In contrast to linear models there do not exist unified methods for nonlinear approaches. In this case conditional maximum likelihood methods dominate for fixed effects models. Under random effects assumptions it is sometimes possible to employ conventional maximum likelihood methods using Gaussian quadrature to reduce a T-dimensional integral. Alternatives are generalized methods of moments and simulated estimators. If the nonlinear function is not exactly known, nonparametric or semiparametric methods should be preferred.
UR - http://www.scopus.com/inward/record.url?scp=84890009628&partnerID=8YFLogxK
U2 - 10.1007/3-540-32693-6_9
DO - 10.1007/3-540-32693-6_9
M3 - Contribution to book/anthology
AN - SCOPUS:84890009628
SN - 3540326928
SN - 9783540326922
SP - 119
EP - 135
BT - Modern Econometric Analysis
ER -