lecture_9_slides

lecture_9_slides - 9-1Assessing Studies Based on Multiple...

Info iconThis preview shows pages 1–8. Sign up to view the full content.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: 9-1Assessing Studies Based on Multiple Regression (SW Chapter 9) Lets step back and take a broader look at regression: Is there a systematic way to assess (critique) regression studies? We know the strengths but what are the pitfalls of multiple regression? When we put all this together, what have we learned about the effect on test scores of class size reduction? 9-2Is there a systematic way to assess regression studies?Multiple regression has some key virtues: It provides an estimate of the effect on Yof arbitrary changes X. It resolves the problem of omitted variable bias, if an omitted variable can be measured and included. It can handle nonlinear relations (effects that vary with the Xs) Still, OLS might yield a biasedestimator of the true causaleffect it might not yield valid inferences 9-3A Framework for Assessing Statistical Studies:Internal and External Validity (SW Section 9.1) Internal validity: the statistical inferences about causal effects are valid for the population being studied. External validity: the statistical inferences can be generalized from the population and setting studied to other populations and settings, where the setting refers to the legal, policy, and physical environment and related salient features. 9-4Threats to External Validity of Multiple Regression Studies How far can we generalize class size results from California school districts? Differences in populations oCalifornia in 2005? oMassachusetts in 2005? oMexico in 2005? Differences in settings odifferent legal requirements concerning special education odifferent treatment of bilingual education odifferences in teacher characteristics 9-5Threats to Internal Validity of Multiple Regression Analysis (SW Section 9.2)Internal validity: the statistical inferences about causal effects are valid for the population being studied. Five threats to the internal validity of regression studies: 1.Omitted variable bias 2.Wrong functional form 3.Errors-in-variables bias 4.Sample selection bias 5.Simultaneous causality bias All of these imply that E(ui|X1i,,Xki) 0 in which case OLS is biased and inconsistent.9-61. Omitted variable bias Omitted variable bias arises if an omitted variable is both: (i) a determinant of Yand (ii) correlated with at least one included regressor. We first discussed omitted variable bias in regression with a single X, but OV bias will arise when there are multiple Xs as well, if the omitted variable satisfies conditions (i) and (ii) above. 9-7Potential solutions to omitted variable bias 1.If the variable can be measured, include it as an additional regressor in multiple regression; 2.Possibly, use panel datain which each entity (individual) is observed more than once; 3.If the variable cannot be measured, use instrumental variables regression; 4.Run a randomized controlled experiment. Why does this work?Remember if Xis randomly assigned, then Xnecessarily will be distributed independently of u; thus E(u|X= x) = 0. 9-8...
View Full Document

This note was uploaded on 05/25/2011 for the course ECON 2007 taught by Professor J during the Spring '11 term at UCL.

Page1 / 34

lecture_9_slides - 9-1Assessing Studies Based on Multiple...

This preview shows document pages 1 - 8. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online