Northwestern University Node
https://hdl.handle.net/1813/34126
2020-07-04T22:39:28ZNCRN Meeting Spring 2016: A 2016 View of 2020 Census Quality, Costs, Benefits
https://hdl.handle.net/1813/43897
NCRN Meeting Spring 2016: A 2016 View of 2020 Census Quality, Costs, Benefits
Spencer, Bruce D.
Census costs affect data quality and data quality affects census benefits. Although measuring census data quality is difficult enough ex post, census planning requires it to be done well in advance. The topic of this talk is the prediction of the cost-quality curve, its uncertainty, and its relation to benefits from census data.
Presented at the NCRN Meeting Spring 2016 in Washington DC on May 9-10, 2016; see http://www.ncrn.info/event/ncrn-spring-2016-meeting
2016-05-10T00:00:00ZCommunicating Uncertainty in Official Economic Statistics: An Appraisal Fifty Years after Morgenstern
https://hdl.handle.net/1813/40830
Communicating Uncertainty in Official Economic Statistics: An Appraisal Fifty Years after Morgenstern
Manski, Charles F.
Federal statistical agencies in the United States and analogous agencies elsewhere commonly report official economic statistics as point estimates, without accompanying measures of error. Users of the statistics may incorrectly view them as error-free or may incorrectly conjecture error magnitudes. This paper discusses strategies to mitigate misinterpretation of official statistics by communicating uncertainty to the public. Sampling error can be measured using established statistical principles. The challenge is to satisfactorily measure the various forms of nonsampling error. I find it useful to distinguish transitory statistical uncertainty, permanent statistical uncertainty, and conceptual uncertainty. I illustrate how each arises as the Bureau of Economic Analysis periodically revises GDP estimates, the Census Bureau generates household income statistics from surveys with nonresponse, and the Bureau of Labor Statistics seasonally adjusts employment statistics. I anchor my discussion of communication of uncertainty in the contribution of Morgenstern (1963), who argued forcefully for agency publication of error estimates for official economic statistics.
2014-10-01T00:00:00ZCommunicating Uncertainty in Official Economic Statistics
https://hdl.handle.net/1813/36323
Communicating Uncertainty in Official Economic Statistics
Manski, Charles
Federal statistical agencies in the United States and analogous agencies elsewhere commonly report official economic statistics as point estimates, without accompanying measures of error. Users of the statistics may incorrectly view them as error-free or may incorrectly conjecture error magnitudes. This paper discusses strategies to mitigate misinterpretation of official statistics by communicating uncertainty to the public. Sampling error can be measured using established statistical principles. The challenge is to satisfactorily measure the various forms of nonsampling error. I find it useful to distinguish transitory statistical uncertainty, permanent statistical uncertainty, and conceptual uncertainty. I illustrate how each arises as the Bureau of Economic Analysis periodically revises GDP estimates, the Census Bureau generates household income statistics from surveys with nonresponse, and the Bureau of Labor Statistics seasonally adjusts employment statistics.
2014-04-01T00:00:00ZCredible interval estimates for official statistics with survey nonresponse
https://hdl.handle.net/1813/34447
Credible interval estimates for official statistics with survey nonresponse
Manski, Charles F.
Government agencies commonly report official statistics based on survey data as point estimates, without accompanying measures of error. In the absence of agency guidance, users of the statistics can only conjecture the error magnitudes. Agencies could mitigate misinterpretation of official statistics if they were to measure potential errors and report them. Agencies could report sampling
error using established statistical principles. It is more challenging to report nonsampling errors because there are many sources of such errors and there has been no consensus about how to measure them. To advance discourse on practical ways to report nonsampling error, this paper considers error
due to survey nonresponse. I summarize research deriving interval estimates that make no assumptions about the values of missing data. In the absence of assumptions, one can obtain computable bounds on the population parameters that official statistics intend to measure. I also explore the middle ground between interval estimation making no assumptions and traditional point
estimation using weights and imputations to implement assumptions that nonresponse is
conditionally random.
I am
grateful to Aanchal Jain for excellent research assistance and to Bruce Spencer for helpful
discussions. I have benefitted from the opportunity to present this work in a seminar at the Institute
for Social and Economic Research, University of Essex.
2013-04-01T00:00:00Z