Inference in linear regression models with many covariates and heteroskedasticity
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- Matias D. Cattaneo & Michael Jansson & Whitney K. Newey, 2015. "Inference in Linear Regression Models with Many Covariates and Heteroskedasticity," Papers 1507.02493, arXiv.org, revised Jan 2017.
References listed on IDEAS
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CitationsCitations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
- Pei, Zhuan & Pischke, Jörn-Steffen & Schwandt, Hannes, 2017.
"Poorly Measured Confounders Are More Useful on the Left Than on the Right,"
IZA Discussion Papers
10647, Institute for the Study of Labor (IZA).
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- Zhuan Pei & Jörn-Steffen Pischke & Hannes Schwandt, 2018. "Poorly Measured Confounders Are More Useful On the Left than On the Right," CEP Discussion Papers dp1539, Centre for Economic Performance, LSE.
- Chaohua Dong & Jiti Gao & Oliver Linton, 2017. "High dimensional semiparametric moment restriction models," Monash Econometrics and Business Statistics Working Papers 17/17, Monash University, Department of Econometrics and Business Statistics.
More about this item
Keywordshigh-dimensional models; linear regression; many regressors; heteroskedastic- ity; standard errors.;
NEP fieldsThis paper has been announced in the following NEP Reports:
- NEP-ALL-2004-04-11 (All new papers)
- NEP-ECM-2004-04-11 (Econometrics)
- NEP-ETS-2004-04-11 (Econometric Time Series)
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