Patrick Kline, Raffaele Saggio, Mikkel Sølvsten
arXiv 5 Jun 2018 · Econometrics · publishedEconometrica (2020) · 180 citations (OpenAlex)
arXiv:1806.01494 · PDF · DOI · OpenAlex · Extracted main text
We propose leave-out estimators of quadratic forms designed for the study of linear models with unrestricted heteroscedasticity. Applications include analysis of variance and tests of linear restrictions in models with many regressors. An approximation algorithm is provided that enables accurate computation of the estimator in very large datasets. We study the large sample properties of our estimator allowing the number of regressors to grow in proportion to the number of observations. Consistency is established in a variety of settings where plug-in methods and estimators predicated on homoscedasticity exhibit first-order biases. For quadratic forms of increasing rank, the limiting distribution can be represented by a linear combination of normal and non-central $\chi^2$ random variables, with normality ensuing under strong identification. Standard error estimators are proposed that enable tests of linear restrictions and the construction of uniformly valid confidence intervals for quadratic forms of interest. We find in Italian social security records that leave-out estimates of a variance decomposition in a two-way fixed effects model of wage determination yield substantially different conclusions regarding the relative contribution of workers, firms, and worker-firm sorting to wage inequality than conventional methods. Monte Carlo exercises corroborate the accuracy of our asymptotic approximations, with clear evidence of non-normality emerging when worker mobility between blocks of firms is limited.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Cattaneo, M. D., M. Jansson, and W. K. Newey (2018) Inference in linear regression models with many covariates and heteroscedasticity | 1.000 | 8 | 3 | 100% |
| 2 | Abowd, J. M., F. Kramarz, and D. N. Margolis (1999) High wage workers and high wage firms | 1.000 | 7 | 3 | 100% |
| 3 | Andrews, M. J., L. Gill, T. Schank, and R. Upward (2008) High wage workers and low wage firms: negative assortative matching or limited mobility bias? | 1.000 | 6 | 3 | 100% |
| 4 | Anatolyev, S (2012) Inference in regression models with many regressors | 1.000 | 5 | 3 | 100% |
| 5 | Newey, W. K. and J. R. Robins (2018) Cross-fitting and fast remainder rates for semiparametric estimation | 0.928 | 4 | 3 | 100% |
| 6 | Searle, S. R., G. Casella, and C. E. McCulloch (2009) Variance components, Volume 391 | 0.928 | 4 | 3 | 100% |
| 7 | Card, D., J. Heining, and P. Kline (2013) Workplace heterogeneity and the rise of west german wage inequality | 0.830 | 7 | 6 | 57% |
| 8 | Achlioptas, D (2003) Database-friendly random projections: Johnson-lindenstrauss with binary coins | 0.737 | 3 | 3 | 67% |
| 9 | Dhaene, G. and K. Jochmans (2015) Split-panel jackknife estimation of fixed-effect models | 0.737 | 3 | 3 | 67% |
| 10 | Hahn, J. and W. Newey (2004) Jackknife and analytical bias reduction for nonlinear panel models | 0.737 | 3 | 3 | 67% |
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