Tantalus on the Road to Asymptopia
My first reaction to "The Credibility Revolution in Empirical Economics," authored by Joshua D. Angrist and Jörn-Steffen Pischke, was: Wow! This paper makes a stunningly good case for relying on purposefully randomized or accidentally randomized experiments to relieve the doubts that afflict inferences from nonexperimental data. On further reflection, I realized that I may have been overcome with irrational exuberance. Moreover, with this great honor bestowed on my "con" article, I couldn't easily throw this child of mine overboard. As Angrist and Pischke persuasively argue, either purposefully randomized experiments or accidentally randomized "natural" experiments can be extremely helpful, but Angrist and Pischke seem to me to overstate the potential benefits of the approach. I begin with some thoughts about the inevitable limits of randomization, and the need for sensitivity analysis in this area, as in all areas of applied empirical work. I argue that the recent financial catastrophe is a powerful illustration of the fact that extrapolating from natural experiments will inevitably be hazardous. I discuss how the difficulties of applied econometric work cannot be evaded with econometric innovations, offering as examples some under-recognized difficulties with instrumental variables and robust standard errors. I conclude with comments about the shortcomings of an experimentalist paradigm as applied to macroeconomics, and some warnings about the willingness of applied economists to apply push-button methodologies without sufficient hard thought regarding their applicability and shortcomings.
Volume (Year): 24 (2010)
Issue (Month): 2 (Spring)
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- White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
- Gary Chamberlain & Guido W. Imbens, 1996.
"Hierarchical Bayes Models with Many Instrumental Variables,"
Harvard Institute of Economic Research Working Papers
1781, Harvard - Institute of Economic Research.
- Gary Chamberlain & Guido W. Imbens, 1996. "Hierarchical Bayes Models with Many Instrumental Variables," NBER Technical Working Papers 0204, National Bureau of Economic Research, Inc.
- Chamberlain, Gary & Imbens, Guido, 1996. "Hierarchical Bayes Models with Many Instrumental Variables," Scholarly Articles 3221489, Harvard University Department of Economics.
- Imbens, Guido W & Angrist, Joshua D, 1994.
"Identification and Estimation of Local Average Treatment Effects,"
Econometric Society, vol. 62(2), pages 467-475, March.
- Joshua D. Angrist & Guido W. Imbens, 1995. "Identification and Estimation of Local Average Treatment Effects," NBER Technical Working Papers 0118, National Bureau of Economic Research, Inc.
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