Robust Standard Errors in Small Samples: Some Practical Advice
نویسندگان
چکیده
منابع مشابه
Robust Standard Errors in Small Samples: Some Practical Advice∗
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 a natural extension of a...
متن کاملRobust Regression Methods: Achieving Small Standard Errors When There Is Heteroscedasticity
A serious practical problem with the ordinary least squares regression estimator is that it can have a relatively large standard error when the error term is heteroscedastic, even under normality. In practical terms, power can be poor relative to other regression estimators that might be used. This article illustrates the problem and summarizes strategies for dealing with it. Included are new r...
متن کاملNoninvasive ventilation: practical advice.
PURPOSE OF REVIEW This critical review discusses the key points that would be of practical help for the clinician who applies noninvasive ventilation (NIV) for treatment of patients with acute respiratory failure (ARF). RECENT FINDINGS In recent years, the growing role of NIV in the acute care setting has led to the development of technical innovations to overcome the problems related to gas ...
متن کاملHeteroskedasticity-Robust Standard Errors for Fixed Effects Panel Data Regression
The conventional heteroskedasticity-robust (HR) variance matrix estimator for cross-sectional regression (with or without a degrees-of-freedom adjustment), applied to the fixed-effects estimator for panel data with serially uncorrelated errors, is inconsistent if the number of time periods T is fixed (and greater than 2) as the number of entities n increases. We provide a bias-adjusted HR estim...
متن کاملBosonSampling is robust against small errors in the network matrix
Citation Arkhipov, Alex. "BosonSampling is robust against small errors in the network matrix. Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Review of Economics and Statistics
سال: 2016
ISSN: 0034-6535,1530-9142
DOI: 10.1162/rest_a_00552