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Missing Measurements in a Regression Problem With No Auxiliary Relations

dc.contributor.authorBalestra, P.en_US
dc.contributor.authorKmenta, Janen_US
dc.date.accessioned2013-11-14T23:21:32Z
dc.date.available2013-11-14T23:21:32Z
dc.date.issued1984en_US
dc.identifier.otherMichU DeptE CenREST RSQE R113.84en_US
dc.identifier.otherC130en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/100843
dc.description.abstractIn this paper it is argued that maximizing the complete data (log) likelihood function with respect to the missing data and the unknown parameters will not improve the efficiency of the estimators but may affect consistency instead. If no auxiliary relations are available or additional assumptions are made, the maximum likelihood estimator based on the observed data is (asymptotically) the most efficient estimator.en_US
dc.description.sponsorshipCenter for Research on Economic and Social Theory, Research Seminar in Quantitative Economics, Department of Economics, University of Michiganen_US
dc.relation.ispartofseriesDiscussion Paperen_US
dc.subjectRegression Modelen_US
dc.subject.otherEstimation: Generalen_US
dc.titleMissing Measurements in a Regression Problem With No Auxiliary Relationsen_US
dc.typeWorking Paperen_US
dc.subject.hlbsecondlevelEconomicsen_US
dc.subject.hlbtoplevelSocial Sciencesen_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/100843/1/ECON029.pdf
dc.owningcollnameEconomics, Department of - Working Papers Series


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