Command For Robust Standard Error In Stata
Contents |
Chapter 4 - Beyond OLS Chapter Outline 4.1 Robust Regression Methods 4.1.1 Regression with Robust Standard Errors 4.1.2 Using the Cluster Option 4.1.3 Robust Regression 4.1.4 Quantile Regression
Stata Robust Standard Errors To Heteroskedasticity
4.2 Constrained Linear Regression 4.3 Regression with Censored or Truncated Data 4.3.1 cluster robust standard errors stata Regression with Censored Data 4.3.2 Regression with Truncated Data 4.4 Regression with Measurement Error 4.5 Multiple Equation Regression robust standard errors spss Models 4.5.1 Seemingly Unrelated Regression 4.5.2 Multivariate Regression 4.6 Summary 4.7 Self assessment 4.8 For more information In this chapter we will go into various commands that go beyond OLS.
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This chapter is a bit different from the others in that it covers a number of different concepts, some of which may be new to you. These extensions, beyond OLS, have much of the look and feel of OLS but will provide you with additional tools to work with linear models. The topics will include robust regression methods, constrained linear regression, regression with censored and truncated data, regression with measurement error, and multiple equation
Robust Standard Errors R
models. 4.1 Robust Regression Methods It seems to be a rare dataset that meets all of the assumptions underlying multiple regression. We know that failure to meet assumptions can lead to biased estimates of coefficients and especially biased estimates of the standard errors. This fact explains a lot of the activity in the development of robust regression methods. The idea behind robust regression methods is to make adjustments in the estimates that take into account some of the flaws in the data itself. We are going to look at three approaches to robust regression: 1) regression with robust standard errors including the cluster option, 2) robust regression using iteratively reweighted least squares, and 3) quantile regression, more specifically, median regression. Before we look at these approaches, let's look at a standard OLS regression using the elementary school academic performance index (elemapi2.dta) dataset. use http://www.ats.ucla.edu/stat/stata/webbooks/reg/elemapi2 We will look at a model that predicts the api 2000 scores using the average class size in K through 3 (acs_k3), average class size 4 through 6 (acs_46), the percent of fully credentialed teachers (full), and the size of the school (enroll). First let's look at the descriptive statistics for these variables. Note the missing values for acs_k3 and acs_k6. summarize api00 acs_k3 acs_46 full enroll Variable | Obs Mean Std. Dev. Min Max ---------+-----------------------------------------------------
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What Are Robust Standard Errors
books Books on Stata Books on statistics Stat/Transfer Stata Journal Gift Shop Training NetCourses when to use clustered standard errors Classroom and web On-site Video tutorials Third-party courses Support Updates Documentation Installation Guide FAQs Register Stata Technical services Policy Contact Publications Bookstore Stata Journal http://www.ats.ucla.edu/stat/stata/webbooks/reg/chapter4/statareg4.htm Stata News Conferences and meetings Stata Conference Upcoming meetings Proceedings Email alerts Statalist The Stata Blog Web resources Author Support Program Installation Qualification Tool Disciplines Company StataCorp Contact us Hours of operation Announcements Customer service Register Stata online Change registration Change address Subscribe to Stata News Subscribe to http://www.stata.com/support/faqs/statistics/references/ email alerts International resellers Careers Our sites Statalist The Stata Blog Stata Press Stata Journal Advanced search Site index Purchase Products Training Support Company >> Home >> Resources & support >> FAQs >> Reference for cluster-correlated robust variance calculation Which references should I cite when using the vce(cluster clustvar) option to obtain Stata’s cluster-correlated robust estimate of variance? Title Citing references for Stata’s cluster-correlated robust variance estimates Author Roberto Gutierrez, StataCorp David M. Drukker, StataCorp Question In performing my statistical analysis, I have used Stata’s _____ estimation command with the vce(cluster clustvar) option to obtain a robust variance estimate that adjusts for within-cluster correlation. A journal referee now asks that I give the appropriate reference for this calculation. Which references should I cite? Short answer Rogers, W. H. 1993. Regression standard errors in clustered samples. Stata