Imputation Methodology for the Agricultural Resource Management - - PowerPoint PPT Presentation

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Imputation Methodology for the Agricultural Resource Management - - PowerPoint PPT Presentation

Jun 24, 2023 331 likes •593 views

Assessing the Impact of a New Imputation Methodology for the Agricultural Resource Management Survey Darcy Miller National Agricultural Statistics Service . . . providing timely, accurate, and useful statistics in service to U.S.

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“. . . providing timely, accurate, and useful statistics in service to U.S. agriculture.”

Assessing the Impact of a New Imputation Methodology for the Agricultural Resource Management Survey

Darcy Miller National Agricultural Statistics Service

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Summer Conference Preview/Review 2014 July 22nd 2014

National Agricultural Statistics Service (NASS)

  • “The National Agricultural Statistics

Service provides timely, accurate, and useful statistics in service to U.S. Agriculture.”

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Summer Conference Preview/Review 2014 July 22nd 2014

Agricultural Resource Management Survey (ARMS)

ARMS is the USDA’s primary survey for the annual collection of data from farm operators

  • Household
  • demographic attributes, labor allocation and debt
  • Farm
  • ownership, management structure, cost and returns, assets

and debt

  • Production Practices
  • tillage, fertilizer, and pesticides

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Background

  • Research effort started in June 2009
  • Cooperative agreement between NASS and

National Institute of Statistical Sciences (NISS)

  • Agreement formed in response to a panel

review by the Committee on National Statistics (CNSTAT)

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Recommendation from CNSTAT

Recommendation 6.7: NASS and ERS should consider approaches for imputation of missing data that would be appropriate when analyzing the data using multivariate

  • models. Methods for accounting for the

variability due to using imputed values should be investigated. Such methods would depend on the imputation approach adopted.

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Current Imputation Methodology

  • Uses conditional mean imputation
  • Form Groups of Operations believed to be

similar (Region, Farm Size, Farm Type)

  • Impute the mean item value of the group for
  • perations in the group with missing values

for that item

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New Imputation Methodology

  • Uses multiple variables in imputation
  • Data are transformed and a regression-based

technique is used

  • Various criteria are used to select the covariates
  • Parameter estimates for the sequence of linear

models and imputations are obtained using Markov chain Monte Carlo

  • Referred to as Iterative Sequential Regression

(ISR)

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Operational Testing

  • R for Operational Use
  • Generalization & User Interface
  • Integrity of Data File
  • Transformations
  • Convergence
  • Impact to Workload
  • Impact to Indications

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R for Operational Use

  • R was approved for operational use by the end
  • f the research project.
  • Server Issues

– Loading – Moving Data Across Platforms

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Generalization & User Interface

  • Parameter Files

– Calculated Variables, Variable Groups, Variable Types, Questionnaire Versions, Transformations, Percents, Income Bins, Notification Email, Seed & Iterations & Imputations

  • SAS Programs

– Convert &Move Data and Run Program – Move Data and Convert Data

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Integrity of Data File

  • Moving data across platforms and software

– Character Values – Rounded Values

  • Correct Cells and Reasonable Values
  • Zeros

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Efficacy of Transformations 2008-2012

  • Achieving Normality (Univariate)
  • Across all years, 2008 to 2012, the

transformations selected produce a reasonable fit for nearly every variable.

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Markov Chain Monte Carlo Convergence Diagnostics 2008-2012

  • Looking across the years 2008 to 2012,

convergence seems to be demonstrated by the 100th iteration for most imputed variables, and by the 200th iteration for most of the remainder.

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Analysis of 2011/2012 Data

  • Evaluated change in workload by analyzing the

critical error counts

  • Examined the 18 key variables after the

summary

  • Looked results for 2011, 2012, and 2012

“collapsed” (covariates summed together)

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Workload Evaluation

  • Analyzed Critical Error Count Differences and

Percent Differences for the following scenarios:

– 2011 ISR vs. 2011 Mean – 2012 ISR vs. 2012 Mean – 2012 ISR Collapsed vs. 2012 Mean

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US Level Results

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Workload Assessment Conclusions

  • Indications that the new ISR method will

somewhat increase workload

  • Indications that collapsing the variables

included in the model will somewhat increase workload compared to the full variable model

  • Indications that adding a couple edits to the

ISR program will not significantly reduce the workload

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Impact to Estimates

  • NASS publishes 18 estimates from data

collected on ARMS III

  • 3 estimates include some imputed data
  • No post edit run after imputation

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Impact to Indications

  • Agricultural Chemicals

Expenditures

  • Farm Improvements and

Construction

  • Farm Services*
  • Farm Supplies and Repairs
  • Feed Expenditures
  • Fertilizer, Lime and Soil

Conditioner Expenditures

  • Fuels Expenditures
  • Interest
  • Labor Expenditures
  • Livestock, Poultry, and

Related Expenses

  • Miscellaneous Capital

Expenses

  • Other Farm Machinery

Expenditures

  • Rent
  • Seeds and Plants
  • Taxes*
  • Total Expenditures*
  • Tractor and Self-Propelled

Farm Machinery Expenditures

  • Trucks and Autos

Expenditures

* Variable contains imputed values

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Calibration Interaction

  • Components that make up GVSALES are imputed.

– i.e. P543 (landlord share gov payments)

  • As GVSALES changes, ECONCLS changes.
  • One of our calibration targets is ECONCLS

– Movement between ECONCLS required us to re-calibrate.

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Other and Future

  • Checks for ill-conditioned matrices
  • Stress Test and Document I/O Functions
  • Tuning Other Parameters of the Program
  • Speed

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“. . . providing timely, accurate, and useful statistics in service to U.S. agriculture.”

Thank You!