Introduction: All the results not fit to print – Why measurement error haunts empirical work in macroeconomics

Abstract

In the late 1980s, I attended a briefing for the Federal Reserve's Board of Governors prior to a meeting of its Federal Open Market Committee. Although employed by the St. Louis Fed, I was spending a week “behind the scenes,” observing how information was assembled and delivered by staff in Washington. During that briefing, when one of the senior staff mentioned that the most recent unemployment figure had changed by one-tenth of one percent. Manley Johnson, the Fed's Vice Chairman, then asked an obvious question: “What's the standard error of that measurement?” Some junior member of the staff said “point six” or, in other words, that any change in the unemployment rate within six-tenths of one percent would fall within the measurement error of the raw data. After an appropriate amount of chuckling rippled through the room at Governor Johnson's important insight, everyone went back to discussing how the recent 0.1 change in the measured unemployment rate should affect the looming monetary policy decision. And so it goes in the world of empirical macroeconomics and the sausage factory that is policy making at the Fed.

Citation

Belongia, M.T. (2009), "Introduction: All the results not fit to print – Why measurement error haunts empirical work in macroeconomics", Binner, J.M., Edgerton, D.L. and Elger, T. (Ed.) Measurement Error: Consequences, Applications and Solutions (Advances in Econometrics, Vol. 24), Emerald Group Publishing Limited, Leeds, pp. ix-xiv. https://doi.org/10.1108/S0731-9053(2009)0000024003