Department of Economics
278 Julis Romo Rabinowitz Building
Princeton, NJ 08544-1021
Institutional Affiliation: Princeton University
NBER Working Papers and Publications
|August 2018||Shift-Share Designs: Theory and Inference|
with , : w24944
We study inference in shift-share regression designs, such as when a regional outcome is regressed on a weighted average of observed sectoral shocks, using regional sector shares as weights. We conduct a placebo exercise in which we estimate the effect of a shift-share regressor constructed with randomly generated sectoral shocks on actual labor market outcomes across U.S. Commuting Zones. Tests based on commonly used standard errors with 5% nominal significance level reject the null of no effect in up to 55% of the placebo samples. We use a stylized economic model to show that this overrejection problem arises because regression residuals are correlated across regions with similar sectoral shares, independently of their geographic location. We derive novel inference methods that are valid...
Published: Rodrigo Adão & Michal Kolesár & Eduardo Morales, 2019. "Shift-Share Designs: Theory and Inference*," The Quarterly Journal of Economics, vol 134(4), pages 1949-2010.
|October 2012||Robust Standard Errors in Small Samples: Some Practical Advice|
with : w18478
In this paper we discuss the properties of confidence intervals for regression parameters based on robust standard errors. We discuss the motivation for a modification suggested by Bell and McCaffrey (2002) to improve the finite sample properties of the confidence intervals based on the conventional robust standard errors. We show that the Bell-McCaffrey modification is the natural extension of a principled approach to the Behrens-Fisher problem, and suggest a further improvement for the case with clustering. We show that these standard errors can lead to substantial improvements in coverage rates even for sample sizes of fifty and more. We recommend researchers calculate the Bell-McCaffrey degrees-of-freedom adjustment to assess potential problems with conventional robust standard errors ...
Published: Guido W. Imbens & Michal Kolesár, 2016. "Robust Standard Errors in Small Samples: Some Practical Advice," Review of Economics and Statistics, vol 98(4), pages 701-712. citation courtesy of
|October 2011||Identification and Inference with Many Invalid Instruments|
with , , , : w17519
We analyze linear models with a single endogenous regressor in the presence of many instrumental variables. We weaken a key assumption typically made in this literature by allowing all the instruments to have direct effects on the outcome. We consider restrictions on these direct effects that allow for point identification of the effect of interest. The setup leads to new insights concerning the properties of conventional estimators, novel identification strategies, and new estimators to exploit those strategies. A key assumption underlying the main identification strategy is that the product of the direct effects of the instruments on the outcome and the effects of the instruments on the endogenous regressor has expectation zero. We argue in the context of two specific examples with a gr...
Published: Michal Kolesár & Raj Chetty & John Friedman & Edward Glaeser & Guido W. Imbens, 2015. "Identification and Inference With Many Invalid Instruments," Journal of Business & Economic Statistics, vol 33(4), pages 474-484. citation courtesy of
|February 2010||Clustering, Spatial Correlations and Randomization Inference|
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It is standard practice in empirical work to allow for clustering in the error covariance matrix if the explanatory variables of interest vary at a more aggregate level than the units of observation. Often, however, the structure of the error covariance matrix is more complex, with correlations varying in magnitude within clusters, and not vanishing between clusters. Here we explore the implications of such correlations for the actual and estimated precision of least squares estimators. We show that with equal sized clusters, if the covariate of interest is randomly assigned at the cluster level, only accounting for non-zero covariances at the cluster level, and ignoring correlations between clusters, leads to valid standard errors and confidence intervals. However, in many cases this m...
Published: Thomas Barrios & Rebecca Diamond & Guido W. Imbens & Michal Kolesï¿½r, 2012. "Clustering, Spatial Correlations, and Randomization Inference," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 578-591, June. citation courtesy of