Arbeitspapier

A Two-Step Estimator for Missing Values in Probit Model Covariates

This paper includes a simulation study on the bias and MSE properties of a two-step probit model estimator for handling missing values in covariates by conditional imputation. In one smaller simulation it is compared with an asymptotically efficient estimator and in one larger it is compared with the probit ML on complete cases after listwise deletion. Simulation results obtained favors the use of the two-step probit estimator and motivates further developments of the methodology.

Language
Englisch

Bibliographic citation
Series: Working Paper ; No. 3/2015

Classification
Wirtschaft
Econometrics
Multiple or Simultaneous Equation Models: Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
Subject
binary variable
imputation
OLS
heteroskedasticity

Event
Geistige Schöpfung
(who)
Laitila, Thomas
Wang, Lisha
Event
Veröffentlichung
(who)
Örebro University School of Business
(where)
Örebro
(when)
2015

Handle
Last update
10.03.2025, 11:41 AM CET

Data provider

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Object type

  • Arbeitspapier

Associated

  • Laitila, Thomas
  • Wang, Lisha
  • Örebro University School of Business

Time of origin

  • 2015

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