Semiparametric Analysis Of Random Effects Linear Models From Binary Panel Data
Charles F. Manski
No 292669, SSRI Workshop Series from University of Wisconsin-Madison, Social Systems Research Institute
Andersen (1970) considered the problem of inference on random effects linear models from binary response panel data. He showed that inference is possible if the disturbances for each panel member are known to be white noise with the logistic distribution and if the observed explanatory variables vary over time. A conditional maximum likelihood estimator consistently estimates the model parameters up to scale. The present note shows that inference remains possible if the disturbances for each panel member are known only to be time-stationary with unbounded support and if the explanatory variables vary enough over time. A conditional version of the maximum score estimator (Manski, 1975, 1985) consistently estimates the model parameters up to scale.
Keywords: Research; Methods/; Statistical; Methods (search for similar items in EconPapers)
References: Add references at CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:ags:uwssri:292669
Access Statistics for this paper
More papers in SSRI Workshop Series from University of Wisconsin-Madison, Social Systems Research Institute Contact information at EDIRC.
Bibliographic data for series maintained by AgEcon Search ().