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The American Community Survey Summary of a Workshop (2001) / Chapter Skim
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3. Combination of Information Across Time
Pages 12-18

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From page 12...
... For these areas, instead of the individual yearly estimates, the Census Bureau proposes to report equally weighted moving averages of the ACS yearly estimates for the most recent 2 to 5 years, depending on the size of the area. (The use of the cross-sectional models suggested above and the possible oversampling of governmental units with less than 2,500 population could reduce, but likely not eliminate, the need for some kind of borrowing of information for the smallest areas.)
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From page 13...
... Borrowing ACS information across time also raises a broader combination-of-information challenge than represented in the discussion ofthe crosssectional models discussed above, since the ACS, most household surveys, and most administrative records data are collected annually (and sometimes monthly)
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From page 14...
... The interesting technical question was therefore how to use the census data to reduce variances without substantially increasing nonsampling error. This was accomplished by using the census data to define regression predictors in the CPS equation and using the fitted CPS equation to carry out empirical Bayes' smoothing, which effectively calibrates the estimates to a CPS basis.
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From page 15...
... The Census Bureau is examining a more recent approach for the county-level model, referred to as the bivariate model, which uses two linked equations, one for the census estimate and one for the CPS estimate. They are both true process plus sampling error models in which true process is modeled using
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From page 16...
... Since the sampling variances in the state model are small, this approach makes little change to the state model, but it has an effect in the county model. A related approach would be to include the use of a measurement error model to link aggregate CPS and census responses, which can be thought of as a restricted form of the bivariate model.
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From page 17...
... FINAL POINTS The various requirements for the development of time-series models for combining information in this context, including estimating the sampling autocorrelation structure, were put forward. The difficulties of parameter 2Kalman filters involve a "state space" representation of a time series, which assumes that a linear model (with an additive error term)
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From page 18...
... In particular, it may not be easy to find an estimation procedure that provides a substantial improvement over the current decision by the Census Bureau to use moving averages. However, the potential remains for improvement, and methods were discussed that might be used to move forward.
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Key Terms

  • census bureau
  • sampling error
  • sampling errors
  • nonsampling error
  • time series

  • true process
  • repeated surveys
  • borrowing information
  • poverty estimates
  • administrative records

  • bivariate model
  • state model
  • equally weighted
  • direct estimates
  • decennial census

  • weighted moving
  • records data
  • census data
  • dependent variable
  • acs information

  • cps equation
  • yearly estimates
  • space variables
  • cps data
  • time delay

  • state space
  • process plus
  • error variance
  • linear model
  • model error


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