Accuracy vs. Precision in Economics

Here's another appendage mercifully sliced from the dissertation proposal. I have no idea why I wrote an elementary discussion of accuracy vs. precision in the first place; upon further review, I'm rather embarrassed that I didn't obliterate it months ago...

In physical science, the terms accuracy and precision have specific meanings generally accepted by practitioners. Accuracy measures how close any single estimate (or average of a group of estimates) is to the true value of interest. Precision measures how close several estimates are to each other.

For an example in economics, an accurate measure of employment growth would be within x% of actual employment growth, with x selected arbitrarily An "equally" precise measure of employment growth would have two standard measures of employment growth generally within x% of each other. Another example of precision of x% would be if a single series were regularly revised upward or downward by that amount.

Revisions of statistics can be treated as a means of gauging the precision of a macroeconomic data series, but not its accuracy.

Both accuracy and precision are objective properties of the measuring tools and processes used; however, measuring accuracy requires a generally accepted benchmark, while measuring precision requires only the data at hand.

Most variables in physical science are estimated using commercially developed tools�telescopes, microscopes, etc�that have been tested under many conditions to yield a known level of accuracy�a level that can then be used as a portion of the total measurement error. In physical science, �measurement error� is addressed through repeated measurement of the same variable under tightly controlled conditions. That, is all biases are assumed to be irrelevant, to cancel out. Repeated measurement is meant to gauge the precision of the particular measurement process with particular tools; accuracy is assumed to not be the concern of statistical analysis.

In economics, �measurement error� is not similarly addressed. The statistical distributions of measurement must cover both accuracy and precision. Most variables in social science are estimated using surveys of individuals, households, and businesses. Each of these units is sampled once, yielding an unknown level of accuracy at the unit level and consequently at higher aggregation levels. Sometimes, as with employment figures, more than one independent estimate is made of the same concept; after reconciling the differences in construction, it is possible to compare the estimates, and develop a measure of the joint precision of the two measurement processes.


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This page contains a single entry by Kevin published on January 13, 2005 4:34 PM.

Ludwig von Mises on Macroeconomic Data was the previous entry in this blog.

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