@article{Morehart:169401,
      recid = {169401},
      author = {Morehart, Mitch and Milkove, Dan and Xu, Yang},
      title = {Multivariate Farm Debt Imputation in the Agricultural  Resource Management Survey (ARMS)},
      address = {2014-07-27},
      number = {329-2016-13074},
      series = {Poster},
      pages = {11},
      month = {Jul},
      year = {2014},
      abstract = {The US Department of Agriculture (USDA), through its ARMS,  collects detailed information from farm operators on debt  used in the farm business. Specific loan characteristics  such as interest
rate, loan term, origination date, type of  loan, loan purpose, and type of financing are collected for  up to the five largest loans. This information is used to  construct portions of the farm sector balance sheet in  addition to supporting research on credit use, farm  solvency, and debt repayment capacity. Valid estimation and  inferences are critical to the generation of this  data,
however, because of sensitivity, is subject to  nonresponse or "do not know." Ignoring item nonresponse  completely, by setting all missing values to zero or by  taking into account only the existing answers will result  in a bias. Imputation, the practice of filling in missing  data with plausible values, can mitigate this bias. This  analysis examines the use of multivariate
techniques for  debt component imputation. This would be an improvement  over the
generalized mean imputation approach used in ARMS  and for many of the debt components the first attempt at  imputation.},
      url = {http://ageconsearch.umn.edu/record/169401},
      doi = {https://doi.org/10.22004/ag.econ.169401},
}