TY  - JOUR
T1  - Communicating Uncertainty in Official Economic Statistics: An Appraisal Fifty Years after Morgenstern
JF  - Journal of Economic Literature
Y1  - 2015
A1  - Manski, Charles F.
KW  - and Organizing Macroeconomic Data; Data Access E23: Macroeconomics: Production
KW  - B22: History of Economic Thought: Macroeconomics C82: Methodology for Collecting
KW  - Estimating
AB  - 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 Oskar Morgenstern (1963a), who argued forcefully for agency publication of error estimates for official economic statistics. (JEL B22, C82, E23)
VL  - 53
UR  - http://www.aeaweb.org/articles.php?doi=10.1257/jel.53.3.631
ER  - 

TY  - RPRT
T1  - Communicating Uncertainty in Official Economic Statistics: An Appraisal Fifty Years after Morgenstern
Y1  - 2014
A1  - Manski, Charles F.
AB  - <p>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.</p>
PB  - Northwestern University
UR  - http://hdl.handle.net/1813/40830
ER  - 

TY  - RPRT
T1  - Credible interval estimates for official statistics with survey nonresponse
Y1  - 2013
A1  - Manski, Charles F.
AB  - 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.
PB  - Northwestern University
UR  - http://hdl.handle.net/1813/34447
ER  -