Estimation of treatment effects with high-dimensional controls
We propose methods for inference on the average effect of a treatment on a scalar outcome in the presence of very many controls. Our setting is a partially linear regression model containing the treatment/policy variable and a large number p of controls or series terms, with p that is possibly much larger than the sample size n, but where only s
|Date of creation:||31 Dec 2011|
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- Alexandre Belloni & D. Chen & Victor Chernozhukov & Christian Hansen, 2010.
"Sparse models and methods for optimal instruments with an application to eminent domain,"
CeMMAP working papers
CWP31/10, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- A. Belloni & D. Chen & V. Chernozhukov & C. Hansen, 2012. "Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain," Econometrica, Econometric Society, vol. 80(6), pages 2369-2429, November.
- Kane, John, 1997. "Myth and measurement: The new economics of the minimum wage : David Card and Alan B. Krueger, New Jersey: Princeton University Press, 1995, x + 422," International Review of Economics & Finance, Elsevier, vol. 6(2), pages 219-222.
- Newey, Whitney K., 1997. "Convergence rates and asymptotic normality for series estimators," Journal of Econometrics, Elsevier, vol. 79(1), pages 147-168, July.
- A. Belloni & V. Chernozhukov & L. Wang, 2011. "Square-root lasso: pivotal recovery of sparse signals via conic programming," Biometrika, Biometrika Trust, vol. 98(4), pages 791-806.
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