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Evaluating Alternative Methods of Dealing with Missing Observations An Economic Application

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  • Onozaka, Yuko

Abstract

This paper compares methods to remedy missing value problems in survey data. The commonly used methods to deal with this issue are to delete observations that have missing values (case-deletion), replace missing values with sample mean (mean imputation), and substitute a fitted value from auxiliary regression (regression imputation). These methods are easy to implement but have potentially serious drawbacks such as bias and inefficiency. In addition, these methods treat imputed values as known so that they ignore the uncertainty due to 'missingness', which can result in underestimating the standard errors. An alternative method is Multiple Imputation (MI). In this paper, Expectation Maximization (EM) and Data Augmentation (DA) are used to create multiple complete datasets, each with different imputed values due to random draws. EM is essentially maximum-likelihood estimation, utilizing the interdependency between missing values and model parameters. DA estimates the distribution of missing values given the observed data and the model parameters through Markov Chain Monte Carlo (MCMC). These multiple datasets are subsequently combined into a single imputation, incorporating the uncertainty due to the missingness. Results from the Monte Carlo experiment using pseudo data show that MI is superior to other methods for the problem posed here.

Suggested Citation

  • Onozaka, Yuko, 2002. "Evaluating Alternative Methods of Dealing with Missing Observations An Economic Application," 2002 Annual meeting, July 28-31, Long Beach, CA 271495, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  • Handle: RePEc:ags:aaea02:271495
    DOI: 10.22004/ag.econ.271495
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    Cited by:

    1. Salon, Deborah, 2006. "Cars and the City: An Investigation of Transportation and Residential Location Choices in New York City," University of California Transportation Center, Working Papers qt1br223vz, University of California Transportation Center.

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