An Assessment of Imputation Methods for the USDA's Agricultural Resource Management Survey

• Activity Number: 229
• Type: Topic Contributed
• Date/Time: Monday, August 2, 2010 : 2:00 PM to 3:50 PM
• Sponsor: Section on Survey Research Methods
• Author(s): Sujit Kumar Ghosh and Barry Goodwin and Joshua D. Habiger*+ and Timothy Paul Keller and Darcy Miller and Michael Robbins and Kirk White
• Companies: North Carolina State University and North Carolina State University and National Agricultural Statistics Service/National Institute of Statistical Sciences and National Agricultural Statistics Service and National Agricultural Statistics Service and National Institute of Statistical Sciences and National Agricultural Statistics Service
• Keywords: multivariate imputation ; ARMS ; missing data

Abstract

This paper provides an assessment of imputations methods applicable to the Agricultural Resource Management Survey data. We find that both iterative and noniterative regression procedures perform better than the current NASS method and an approximate Bayesian bootstrap method. Both regression methods perform better when utilizing a log-skew-normal transformation over a log-normal transformation.