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On Least Squares Estimation When the Dependent Variable is Grouped

  • Mark B. Stewart

    (University of Warwick and Princeton University)

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    This paper examines the problem of estimating the parameters of an underlying linear model using data in which the dependent variable is only observed to fall in a certain interval on a continuous scale, its actual value remaining unobserved. A Least Squares algorithm for attaining the Maximum Likelihood estimator is described, the asymptotic bias of the OLS estimator derived for the normal regressors case and a "moment" estimator presented. A "two-step estimator" based on combining the two approaches is proposed and found to perform well in both an economic illustration and simulation experiments.

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    File URL: http://arks.princeton.edu/ark:/88435/dsp01j9602061c
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    Paper provided by Princeton University, Department of Economics, Industrial Relations Section. in its series Working Papers with number 539.

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    Date of creation: Nov 1982
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    Handle: RePEc:pri:indrel:159
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