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

Thomas Laitila () and Lisha Wang ()
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Thomas Laitila: Örebro University School of Business, Postal: Örebro University, School of Business, SE - 701 82 ÖREBRO, Sweden, http://www.oru.se/Personal/thomas_laitila/
Lisha Wang: Örebro University School of Business, Postal: Örebro University, School of Business, SE - 701 82 ÖREBRO, Sweden

No 2015:3, Working Papers from Örebro University, School of Business

Abstract: 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 ecient 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.

Keywords: binary variable; imputation; OLS; heteroskedasticity (search for similar items in EconPapers)
JEL-codes: C01 C35 (search for similar items in EconPapers)
Pages: 17 pages
Date: 2015-04-27
New Economics Papers: this item is included in nep-ecm and nep-ore
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