by Leverage is a measure of the degree to which a single observation on the right-hand-side variable takes on extreme values and is influential in estimating the slope of the regression line. A concentration of leverage in even a few observations can make coefficients and standard errors extremely volatile and even bias robust standard errors towards zero, leading to higher rejection rates. To illustrate this problem, Young (2019) went through a simple exercise. He collected over fifty experimental (lab and field) articles from the American Economic Association’s flagship journals: American Economic Review, American Economic Journal: Applied, and American Economic Journal: Economic Policy. He then reanalyzed these papers, using the authors’ models, by dropping one observation
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Lars Pålsson Syll considers the following as important: Statistics & Econometrics
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Leverage is a measure of the degree to which a single observation on the right-hand-side variable takes on extreme values and is influential in estimating the slope of the regression line. A concentration of leverage in even a few observations can make coefficients and standard errors extremely volatile and even bias robust standard errors towards zero, leading to higher rejection rates.
To illustrate this problem, Young (2019) went through a simple exercise. He collected over fifty experimental (lab and field) articles from the American Economic Association’s flagship journals: American Economic Review, American Economic Journal: Applied, and American Economic Journal: Economic Policy. He then reanalyzed these papers, using the authors’ models, by dropping one observation or cluster and reestimating the entire model, repeatedly. What he found was shocking:
“With the removal of just one observation, 35% of 0.01-significant reported results in the average paper can be rendered insignificant at that level. Conversely, 16% of 0.01-insignificant reported results can be found to be significant at that level. “