Imputation in High-Dimensional Economic Data as Applied to the Agricultural Resource Management Survey

Michael W. Robbins, Sujit K. Ghosh and Joshua D. Habiger

Journal of the American Statistical Association, 2013, vol. 108, issue 501, 81-95

Abstract: In this article, we consider imputation in the USDA's Agricultural Resource Management Survey (ARMS) data, which is a complex, high-dimensional economic dataset. We develop a robust joint model for ARMS data, which requires that variables are transformed using a suitable class of marginal densities (e.g., skew normal family). We assume that the transformed variables may be linked through a Gaussian copula, which enables construction of the joint model via a sequence of conditional linear models. We also discuss the criteria used to select the predictors for each conditional model. For the purpose of developing an imputation method that is conducive to these model assumptions, we propose a regression-based technique that allows for flexibility in the selection of conditional models while providing a valid joint distribution. In this procedure, labeled as iterative sequential regression (ISR), parameter estimates and imputations are obtained using a Markov chain Monte Carlo sampling method. Finally, we apply the proposed method to the full ARMS data, and we present a thorough data analysis that serves to gauge the appropriateness of the resulting imputations. Our results demonstrate the effectiveness of the proposed algorithm and illustrate the specific deficiencies of existing methods. Supplementary materials for this article are available online.

Date: 2013
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Citations: View citations in EconPapers (10)

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DOI: 10.1080/01621459.2012.734158

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