Estimation of Causal Effects with Many Covariates

Whitney Newey

<p>The linear regression model is widely used in empirical work. Researchers often include many covariates to control for observed and unobserved confounders. Often the number of covariates may be an important fraction of the sample size. We consider corresponding asymptotics where the number of covariates grows as fast as sample size. We show asymptotic normality and give consistent standard errors. With homoscedasticity we find that the usual standard errors with a degrees of freedom correction are correct. We also give new heteroskedasticity consistent standard errors, and show that the usual Eicker-White standard errors are inconsistent. These results add to regression theory where previous asymptotic normality results restricted the number of regressors to grow slower than the sample size.</p>

2015-12-16(Wednesday)16:40-18:00

N303, Econ Building

English

WISE & SOE

系列讲座

<p>Jane Berkowitz Carlton and Dennis William Carlton Professor of Microeconomics; Chair, MIT Economics</p> <p><a href="/Upload/File/2015/12/20151209092034705.pdf"><u><strong><span style="color: rgb(0, 0, 255);">Prof. Whitney Newey's CV</span></strong></u></a></p>

厦门大学高级计量经济学与统计学系列讲座2015秋季学期第八讲（总第71讲）暨厦门大学南强学术讲座第710期