Applied Quantitative Methods II


 Rosemary Gray
 1 years ago
 Views:
Transcription
1 Applied Quantitative Methods II Lecture 7: Endogeneity and IVs Klára Kaĺıšková Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 1 / 36
2 Outline 1 OLS and the treatment effect 2 OLS and endogeneity 3 Dealing with endogeneity 4 Instrumental variables Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 2 / 36
3 OLS assumptions: short reminder The regression model is linear in the coefficients, is correctly specified, and has an additive error term The error term has a zero population mean Observations of the error term are uncorrelated with each other The error term has a constant variance All explanatory variables are uncorrelated with the error term No explanatory variable is a perfect linear function of any other explanatory variable(s) (The error term is normally distributed) Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 3 / 36
4 Does the OLS estimate treatment effect? If the OLS assumptions hold, OLS is BLUE and estimates the treatment effect correctly Assumption of no correlation b/w explanatory variables and error term crucial! Variables correlated with error term are endogenous variables their coefficients are inconsistent and biased OLS does not estimate the treatment effect If x and the error term are correlated, OLS estimate attributes to x some of the variation in y that actually comes form the error term Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 4 / 36
5 Outline 1 OLS and the treatment effect 2 OLS and endogeneity 3 Dealing with endogeneity 4 Instrumental variables Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 5 / 36
6 Typical cases of endogeneity 1. Omitted variables 2. Selection explanatory variable is not in the equation makes part of the error term unobservable characteristics influence both participation in treatment (dependent variable) and explanatory variables most important and widespread problem in treatment effect estimation 3. Simultaneity  reversed causation relationship between dependent variable and explanatory variable goes in both directions 4. Measurement error Some of independent variables measured with (a nonrandom) error Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 6 / 36
7 Example 1: Class size and test scores Classroom size and test scores Goal: Estimate the effect of class size on educational attainment of children test scores = α + βclass size + ɛ Problems: Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 7 / 36
8 Example 1: Class size and test scores Classroom size and test scores Goal: Estimate the effect of class size on educational attainment of children test scores = α + βclass size + ɛ Problems: 1 Omitted variables Unobserved factors influencing test scores that are correlated with class size e.g. funding, teachers ability, students motivations... 2 Reverse causality Students with low test scores might be put into a smaller classes 3 Measurement error testscores done with penandpaper, which brings errors on Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 7 / 36
9 1. Regression with Omitted variables Classroom size and test scores Suppose the true model is test scores = α + βclass size + δfunding + ω But we don t include funding in the estimation test scores = α + βclass size + ɛ now residuals include funding: ɛ = δfunding + ω and funding is correlated with classsize: E[ɛ class size] 0 richer schools may pay more teachers smaller classes negative corr. BIAS: We expect β < 0, and OLS estimate will overstate the true value: β OLS = 10: part of this negative correlation is due to negative corr. of funding we overstate the true effect (negative bias): β TRUE = 5 Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 8 / 36
10 1. Omitted variables example Example IQ in Africa Do people in Africa have genetically lower IQ scores? IQ score = α + βafrica + ɛ Forgetting education! Education attainment is much lower in Africa and education affects the IQ scores Q: What bias will we have, if we assume β TRUE = 0? Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 9 / 36
11 1. Omitted variables example Example IQ in Africa Do people in Africa have genetically lower IQ scores? IQ score = α + βafrica + ɛ Forgetting education! Education attainment is much lower in Africa and education affects the IQ scores Q: What bias will we have, if we assume β TRUE = 0? A: Negative bias (e.g.β OLS = 10) which can lead to wrong policy conclusions! Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 9 / 36
12 2. Regression with reversed causality Eli Berman  Can Hearts and Minds be bought? Goal: Effect of development spending on violence in Afghanistan violence = α + βspending + ɛ We expect β < 0 but we get β > 0: Q: Why? A: regions with more violence get more money twoway relationship the reversed effect dominates result  positive bias of OLS estimate Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 10 / 36
13 3. Regression with measurement error ME in independent variable! Goal: Does spending on advertising increase sales? The true model: sales t = α + βa t + ɛ Assume e.g. β TRUE = 10 What if advertising measured with error? measured advertising:m t = A t + v t correlation with sales will be weaker: β OLS = 7 if really high measurement error, we may not find any sig. effect at all Results in downward bias (also called attenuation bias) sales t = α + β(m t v t ) + ɛ sales t = α + β 1 M t + (β 2 v + ɛ) Measurement error in dependent variable leads to inefficiency, not bias or inconsistency Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 11 / 36
14 4. Regression with selfselection Angrist (1991): Vietnam war Goal: Estimate effect of being in army (MP  0/1) on lifetime earnings (LI) LI = α + βmp ɛ Idea: Military participation should decrease lifetime earnings: β TRUE < 0 MP depends on many unobserved factors in error term that are crucial for LI (selfselection): Who goes voluntarily into army? Does he like office work? Value money? Academic attitudes? People who choose to go to army are likely to be the ones who would have lower earnings anyway  positive or negative bias? Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 12 / 36
15 4. Regression with selfselection Angrist (1991): Vietnam war Goal: Estimate effect of being in army (MP  0/1) on lifetime earnings (LI) LI = α + βmp ɛ Idea: Military participation should decrease lifetime earnings: β TRUE < 0 MP depends on many unobserved factors in error term that are crucial for LI (selfselection): Who goes voluntarily into army? Does he like office work? Value money? Academic attitudes? People who choose to go to army are likely to be the ones who would have lower earnings anyway  positive or negative bias? OLS estimates upward biased: it will we bigger in absolute value (more negative), because it includes the negative correlation caused by selfselection Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 12 / 36
16 Outline 1 OLS and the treatment effect 2 OLS and endogeneity 3 Dealing with endogeneity 4 Instrumental variables Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 13 / 36
17 Ways to deal with endogeneity 1 Controlled (social) experiment RCT: Direct randomization of treated and untreated 2 Natural experiment Finding naturally occurring treated and untreated group that are as similar as possible  usually leads to diffindiffs method 3 Propensity score matching If treatment assignment only depends on observable characteristics 4 Instrumental variable (IV) Finding variable that assigns into treatment but does not affect outcome 5 Discontinuity design Probability of treatment is changing discontinuously with a characteristic (i.e. age) 6 Panel data can solve endogeneity if unobserved characteristics are timeinvariant Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 14 / 36
18 Dealing with endogeneity: Example Angrist (1991): Vietnam war Goal: Estimate effect of being in army on lifetime earnings LI = α + βmp ɛ Story: Some soldiers volunteered, but it was not enough lottery was used as a mechanism to choose who goes to war they assigned a random number to each day of year and if the number is above some threshold, all men of sufficient age born that day are selected to go (eligibility:z i = 1) lottery  random and exogenous selection of eligibility some were eligible but not participated (health reasons...) some were not eligible but volunteered still, the lottery had a substantial impact on who went to war Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 15 / 36
19 Dealing with endogeneity: Example Angrist (1991): Vietnam war Individual level: Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 16 / 36
20 Dealing with endogeneity: Example Angrist (1991): Vietnam war Intuition: eligibility (lottery result) highly correlated with participation eligibility not connected with any unobserved factors that affect lifetime earnings Use eligibility as an instrument for participation in military comparing lifetime earnings of those who went to war becasue they were eligible (chosen) with those who were not difference in earnings then solely due to military participation, not selfselection! Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 17 / 36
21 Outline 1 OLS and the treatment effect 2 OLS and endogeneity 3 Dealing with endogeneity 4 Instrumental variables Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 18 / 36
22 Instrumental variable An instrumental variable (IV) for X is one solution to the endogenity problem Requirements for Z to be a valid instrument for X : Relevant = Correlated with X Exogenous = Not correlated with Y in other way except through its correlation with X Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 19 / 36
23 Terminology First stage: X = α 0 + α 1 Z + ω Structural model:y = β 0 + β 1 X + ɛ Reduced form: Y = δ 0 + δ 1 Z + ξ an interesting equality follows: δ 1 = α 1 β 1 so β 1 = δ 1 /α 1 Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 20 / 36
24 Are IV requirements satisfied? Is IV relevant? How can we detect weak (not relevant) IVs? Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 21 / 36
25 Are IV requirements satisfied? Is IV relevant? How can we detect weak (not relevant) IVs? Use intuition  is it likely that X and Z are correlated? Formal test: look at the first stage regression  Fstatistics should be >10 Weak IV estimates are not consistent! Do not use weak IVs! Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 21 / 36
26 Are IV requirements satisfied? Is IV relevant? How can we detect weak (not relevant) IVs? Use intuition  is it likely that X and Z are correlated? Formal test: look at the first stage regression  Fstatistics should be >10 Weak IV estimates are not consistent! Do not use weak IVs! Is IV exogenous? Can we test for exogeneity? Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 21 / 36
27 Are IV requirements satisfied? Is IV relevant? How can we detect weak (not relevant) IVs? Use intuition  is it likely that X and Z are correlated? Formal test: look at the first stage regression  Fstatistics should be >10 Weak IV estimates are not consistent! Do not use weak IVs! Is IV exogenous? Can we test for exogeneity? Use intuition! Is there any way Z can be correlated with unobserved determinants of Y? There is not formal test for exogeneity! (see next slide) Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 21 / 36
28 Can we formally test for exogeneity? Is it true that a good instrument should not be correlated with the dependent variable? Can we look at correlation of Y and Z to prove if Z is indeed exogenous? Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 22 / 36
29 Can we formally test for exogeneity? Is it true that a good instrument should not be correlated with the dependent variable? Can we look at correlation of Y and Z to prove if Z is indeed exogenous? No!Z has to be correlated with Y, otherwise it is useless as an instrument But it can only be correlated with Y through X A good instrument must not be correlated with the unobserved determinants of Y otherwise inconsistent! Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 22 / 36
30 Bad instruments 1 Wage and education We estimate wage i = α + βeduc i + u i with an instrument parent educ i Requirments for good IV: 1 There should be some correlation between parental education and own education. Is it likely that more educated parents have more educated children? Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 23 / 36
31 Bad instruments 1 Wage and education We estimate wage i = α + βeduc i + u i with an instrument parent educ i Requirments for good IV: 1 There should be some correlation between parental education and own education. Is it likely that more educated parents have more educated children? Educated parents may provide motivation, role models, better start,... OK Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 23 / 36
32 Bad instruments 1 Wage and education We estimate wage i = α + βeduc i + u i with an instrument parent educ i Requirments for good IV: 1 There should be some correlation between parental education and own education. Is it likely that more educated parents have more educated children? Educated parents may provide motivation, role models, better start,... OK 2 Parental education should not be correlated with unobserved factors determining wages. Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 23 / 36
33 Bad instruments 1 Wage and education We estimate wage i = α + βeduc i + u i with an instrument parent educ i Requirments for good IV: 1 There should be some correlation between parental education and own education. Is it likely that more educated parents have more educated children? Educated parents may provide motivation, role models, better start,... OK 2 Parental education should not be correlated with unobserved factors determining wages. What about parental income? Parental income is likely to be positively related to children s wages and parental education  not OK! Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 23 / 36
34 Bad instruments 2 Classroom attendance and test scores We estimate test scores = α + βclass attendance + u i unobserved: ability, interest more interested/able in class more likely to attend Instrument: being pregnant Conditions: 1 There should be correlation b/w pregnancy and class attendance. Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 24 / 36
35 Bad instruments 2 Classroom attendance and test scores We estimate test scores = α + βclass attendance + u i unobserved: ability, interest more interested/able in class more likely to attend Instrument: being pregnant Conditions: 1 There should be correlation b/w pregnancy and class attendance. pregnant will miss classes often  negative correlation  OK Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 24 / 36
36 Bad instruments 2 Classroom attendance and test scores We estimate test scores = α + βclass attendance + u i unobserved: ability, interest more interested/able in class more likely to attend Instrument: being pregnant Conditions: 1 There should be correlation b/w pregnancy and class attendance. pregnant will miss classes often  negative correlation  OK 2 Pregnancy should not be correlated with unobserved factors affecting test scores. Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 24 / 36
37 Bad instruments 2 Classroom attendance and test scores We estimate test scores = α + βclass attendance + u i unobserved: ability, interest more interested/able in class more likely to attend Instrument: being pregnant Conditions: 1 There should be correlation b/w pregnancy and class attendance. pregnant will miss classes often  negative correlation  OK 2 Pregnancy should not be correlated with unobserved factors affecting test scores. teenage pregnancies prob. associated with lower interest/ability those less interested more likely to get pregnant?  not OK Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 24 / 36
38 Estimation:Twostage least squares (2SLS or TSLS) Step 1: X = a 0 + a 1 Z 1 + a 2 Z a k Z k + a Q Q + u Step 2: Y = b 0 + b 1 X + b 3 Q+e Substitute the fitted X in place of original X Note: if done manually in two stages, standard errors are based on wrong residuals e = Y b 0 b 1 X when it should be e = Y b 0 b 1 X Let the software do it for you ;) Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 25 / 36
39 Control variables in 2SLS model Control variables should be in both stages Stage 1: X = a 0 + a 1 Z + a 2 Q + u Stage 2: Y = b 0 + b 1 X + b 2 Q + e Control vars considered instruments, they are just not excluded instruments they are not excluded from the second stage they are their own instrument Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 26 / 36
40 Example Classroom attendance and test scores We estimate test scores = α + βclass attendance + γsat + u i unobserved: ability, interest more interested/able in class more likely to attend Instrument: distance, public transport quality Procedure: 1 Run class attendance = δ 0 + δ 1 SAT + δ 2 dist + δ 3 pubtrans + v i 2 Obtain fitted values: class attendance ˆ = ˆδ 0 + ˆδ 1 SAT + ˆδ 2 dist + ˆδ 3 pubtrans 3 Run test scores = α + βclass attendance ˆ + γsat + u i Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 27 / 36
41 Finding an instrument Geography as an instrument distance, rivers, small area variation Legal/political institutions as an instrument laws, election dynamics Administrative rules as an instrument wage/staffing rules, reimbursement rules, eligibility rules Naturally occurring randomization draft, birth date, lottery, roommate assignment, weather Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 28 / 36
42 How IV estimation works Not all of the available variation in X is used Only that portion of variation in X,which is explained by Z, is used to explain Y Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 29 / 36
43 How IV estimation works Reality: Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 30 / 36
44 What IV estimation measures? The average treatment effect for individuals who can be induced to change [treatment] status by a change in the instrument Imbens and Angrist (1994) Local Average Treatment Effect (LATE) LATE is instrumentdependent different instruments give different estimates! LATE is the average causal effect of X on Y for compliers people affected by instrument (as opposed to always takers or never takers ) Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 31 / 36
45 Example: Proximity to college study and LATE Who are the compliers? Proximity to college means lower costs of college education Lowincome individuals might base their decision to go to college on college proximity Who are nevertakers? Individual that never go to college, no matter how close they live Who are alwaystakers? Individuals that always go to college, no matter how close they live (smart and/or rich) Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 32 / 36
46 Important point about IV models The IV estimator is biased E(b IV ) β but with endogeneity: E(b LS ) β, plim(b IV ) β anyway The good thing is, IV estimator is consistent E(b IV ) β as N IV studies thus need large samples Asymptotic behavior or IV plim(b IV ) = β + Cov(Z, e)/cov(z, X ) if Z truly exogenous, then Cov(Z, e) = 0 Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 33 / 36
47 Examples of IV Studies Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 34 / 36
48 Conclusion When can we use IVs? What is the underlying idea? What are the basic conditions? Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 35 / 36
49 Example: Police Hiring Goal: Estimate effect of police force on crime rate at regional level Problems: 1 Measurement error crime = α + βpolice + ɛ Mobilization of police officers (M.E. in X ) as well as differential crime reporting (M.E. in Y ) 2 Simultaneity More police might be hired during a crime wave 3 Omitted variables Unobserved characteristics of regions that affect crime rate e.g. severity of punishment, activity of police Klára Kaĺıšková AQM II  Lecture 7 VŠE, SS 2016/17 36 / 36
Instrumental Variables Estimation: An Introduction
Instrumental Variables Estimation: An Introduction Susan L. Ettner, Ph.D. Professor Division of General Internal Medicine and Health Services Research, UCLA The Problem The Problem Suppose you wish to
More informationInstrumental Variables I (cont.)
Review Instrumental Variables Observational Studies Cross Sectional Regressions Omitted Variables, Reverse causation Randomized Control Trials Difference in Difference Time invariant omitted variables
More informationMethods for Addressing Selection Bias in Observational Studies
Methods for Addressing Selection Bias in Observational Studies Susan L. Ettner, Ph.D. Professor Division of General Internal Medicine and Health Services Research, UCLA What is Selection Bias? In the regression
More informationEMPIRICAL STRATEGIES IN LABOUR ECONOMICS
EMPIRICAL STRATEGIES IN LABOUR ECONOMICS University of Minho J. Angrist NIPE Summer School June 2009 This course covers core econometric ideas and widely used empirical modeling strategies. The main theoretical
More informationCarrying out an Empirical Project
Carrying out an Empirical Project Empirical Analysis & Style Hint Special program: Pretraining 1 Carrying out an Empirical Project 1. Posing a Question 2. Literature Review 3. Data Collection 4. Econometric
More informationTechnical Track Session IV Instrumental Variables
Impact Evaluation Technical Track Session IV Instrumental Variables Christel Vermeersch Beijing, China, 2009 Human Development Human Network Development Network Middle East and North Africa Region World
More informationCitation for published version (APA): Ebbes, P. (2004). Latent instrumental variables: a new approach to solve for endogeneity s.n.
University of Groningen Latent instrumental variables Ebbes, P. IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document
More informationEstablishing Causality Convincingly: Some Neat Tricks
Establishing Causality Convincingly: Some Neat Tricks Establishing Causality In the last set of notes, I discussed how causality can be difficult to establish in a straightforward OLS context If assumptions
More information1 Simple and Multiple Linear Regression Assumptions
1 Simple and Multiple Linear Regression Assumptions The assumptions for simple are in fact special cases of the assumptions for multiple: Check: 1. What is external validity? Which assumption is critical
More informationLecture II: Difference in Difference and Regression Discontinuity
Review Lecture II: Difference in Difference and Regression Discontinuity it From Lecture I Causality is difficult to Show from cross sectional observational studies What caused what? X caused Y, Y caused
More informationPublic Policy & Evidence:
Public Policy & Evidence: How to discriminate, interpret and communicate scientific research to better inform society. Rachel Glennerster Executive Director JPAL Global Press coverage of microcredit:
More informationEC352 Econometric Methods: Week 07
EC352 Econometric Methods: Week 07 Gordon Kemp Department of Economics, University of Essex 1 / 25 Outline Panel Data (continued) Random Eects Estimation and Clustering Dynamic Models Validity & Threats
More informationProblem Set 5 ECN 140 Econometrics Professor Oscar Jorda. DUE: June 6, Name
Problem Set 5 ECN 140 Econometrics Professor Oscar Jorda DUE: June 6, 2006 Name 1) Earnings functions, whereby the log of earnings is regressed on years of education, years of onthejob training, and
More informationIntroduction to Program Evaluation
Introduction to Program Evaluation Nirav Mehta Assistant Professor Economics Department University of Western Ontario January 22, 2014 Mehta (UWO) Program Evaluation January 22, 2014 1 / 28 What is Program
More informationSOME NOTES ON THE INTUITION BEHIND POPULAR ECONOMETRIC TECHNIQUES
SOME NOTES ON THE INTUITION BEHIND POPULAR ECONOMETRIC TECHNIQUES These notes have been put together by Dave Donaldson (EC307 class teacher 200405) and by Hannes Mueller (EC307 class teacher 2004present).
More informationThe Limits of Inference Without Theory
The Limits of Inference Without Theory Kenneth I. Wolpin University of Pennsylvania Koopmans Memorial Lecture (2) Cowles Foundation Yale University November 3, 2010 Introduction Fuller utilization of the
More informationPros. University of Chicago and NORC at the University of Chicago, USA, and IZA, Germany
Dan A. Black University of Chicago and NORC at the University of Chicago, USA, and IZA, Germany Matching as a regression estimator Matching avoids making assumptions about the functional form of the regression
More informationPropensity Score Analysis Shenyang Guo, Ph.D.
Propensity Score Analysis Shenyang Guo, Ph.D. Upcoming Seminar: April 78, 2017, Philadelphia, Pennsylvania Propensity Score Analysis 1. Overview 1.1 Observational studies and challenges 1.2 Why and when
More informationWrite your identification number on each paper and cover sheet (the number stated in the upper right hand corner on your exam cover).
STOCKHOLM UNIVERSITY Department of Economics Course name: Empirical methods 2 Course code: EC2402 Examiner: Per PetterssonLidbom Number of credits: 7,5 credits Date of exam: Sunday 21 February 2010 Examination
More informationSession 3: Dealing with Reverse Causality
Principal, Developing Trade Consultants Ltd. ARTNeT Capacity Building Workshop for Trade Research: Gravity Modeling Thursday, August 26, 2010 Outline Introduction 1 Introduction Overview Endogeneity and
More informationMEA DISCUSSION PAPERS
Inference Problems under a Special Form of Heteroskedasticity Helmut Farbmacher, Heinrich Kögel 032015 MEA DISCUSSION PAPERS mea Amalienstr. 33_D80799 Munich_Phone+49 89 38602355_Fax +49 89 38602390_www.mea.mpisoc.mpg.de
More informationDo children in private Schools learn more than in public Schools? Evidence from Mexico
MPRA Munich Personal RePEc Archive Do children in private Schools learn more than in public Schools? Evidence from Mexico Florian Wendelspiess Chávez Juárez University of Geneva, Department of Economics
More informationLecture II: Difference in Difference. Causality is difficult to Show from cross
Review Lecture II: Regression Discontinuity and Difference in Difference From Lecture I Causality is difficult to Show from cross sectional observational studies What caused what? X caused Y, Y caused
More informationAssessing Studies Based on Multiple Regression. Chapter 7. Michael Ash CPPA
Assessing Studies Based on Multiple Regression Chapter 7 Michael Ash CPPA Assessing Regression Studies p.1/20 Course notes Last time External Validity Internal Validity Omitted Variable Bias Misspecified
More informationWhat is Multilevel Modelling Vs Fixed Effects. Will Cook Social Statistics
What is Multilevel Modelling Vs Fixed Effects Will Cook Social Statistics Intro Multilevel models are commonly employed in the social sciences with data that is hierarchically structured Estimated effects
More informationChapter Eight: Multivariate Analysis
Chapter Eight: Multivariate Analysis Up until now, we have covered univariate ( one variable ) analysis and bivariate ( two variables ) analysis. We can also measure the simultaneous effects of two or
More informationSession 1: Dealing with Endogeneity
Niehaus Center, Princeton University GEM, Sciences Po ARTNeT Capacity Building Workshop for Trade Research: Behind the Border Gravity Modeling Thursday, December 18, 2008 Outline Introduction 1 Introduction
More informationA NONTECHNICAL INTRODUCTION TO REGRESSIONS. David Romer. University of California, Berkeley. January Copyright 2018 by David Romer
A NONTECHNICAL INTRODUCTION TO REGRESSIONS David Romer University of California, Berkeley January 2018 Copyright 2018 by David Romer CONTENTS Preface ii I Introduction 1 II Ordinary Least Squares Regression
More informationCorrelation Ex.: Ex.: Causation: Ex.: Ex.: Ex.: Ex.: Randomized trials Treatment group Control group
Ch. 3 1 Public economists use empirical tools to test theory and estimate policy effects. o Does the demand for illicit drugs respond to price changes (what is the elasticity)? o Do reduced welfare benefits
More informationDoing Quantitative Research 26E02900, 6 ECTS Lecture 6: Structural Equations Modeling. OlliPekka Kauppila Daria Kautto
Doing Quantitative Research 26E02900, 6 ECTS Lecture 6: Structural Equations Modeling OlliPekka Kauppila Daria Kautto Session VI, September 20 2017 Learning objectives 1. Get familiar with the basic idea
More informationMachine Learning Statistical Learning. Prof. Matteo Matteucci
Machine Learning Statistical Learning Pro. Matteo Matteucci Statistical Learning Outline o What Is Statistical Learning? Why estimate? How do we estimate? The tradeo between prediction accuracy & model
More informationIntroduction to Applied Research in Economics Kamiljon T. Akramov, Ph.D. IFPRI, Washington, DC, USA
Introduction to Applied Research in Economics Kamiljon T. Akramov, Ph.D. IFPRI, Washington, DC, USA Training Course on Applied Econometric Analysis June 1, 2015, WIUT, Tashkent, Uzbekistan Why do we need
More informationChapter Eight: Multivariate Analysis
Chapter Eight: Multivariate Analysis Up until now, we have covered univariate ( one variable ) analysis and bivariate ( two variables ) analysis. We can also measure the simultaneous effects of two or
More information1.4  Linear Regression and MS Excel
1.4  Linear Regression and MS Excel Regression is an analytic technique for determining the relationship between a dependent variable and an independent variable. When the two variables have a linear
More informationOrdinary Least Squares Regression
Ordinary Least Squares Regression March 2013 Nancy Burns (nburns@isr.umich.edu)  University of Michigan From description to cause Group Sample Size Mean Health Status Standard Error Hospital 7,774 3.21.014
More informationProblem set 2: understanding ordinary least squares regressions
Problem set 2: understanding ordinary least squares regressions September 12, 2013 1 Introduction This problem set is meant to accompany the undergraduate econometrics video series on youtube; covering
More information[En français] For a pdf of the transcript click here.
[En français] For a pdf of the transcript click here. Go back to the podcast on endogeneity: http://www.youtube.com/watch?v=dlutjoymfxs [00:05] Hello, my name is John Antonakis. I am a professor of Organisational
More informationThe Stable Unit Treatment Value Assumption (SUTVA) and Its Implications for Social Science RCTs
The Stable Unit Treatment Value Assumption (SUTVA) and Its Implications for Social Science RCTs Alan S. Gerber & Donald P. Green Yale University From Chapter 8 of Field Experimentation: Design, Analysis,
More informationIntroduction to Econometrics
Global edition Introduction to Econometrics Updated Third edition James H. Stock Mark W. Watson MyEconLab of Practice Provides the Power Optimize your study time with MyEconLab, the online assessment and
More informationWhat is: regression discontinuity design?
What is: regression discontinuity design? Mike Brewer University of Essex and Institute for Fiscal Studies Part of Programme Evaluation for Policy Analysis (PEPA), a Node of the NCRM Regression discontinuity
More informationMarno Verbeek Erasmus University, the Netherlands. Cons. Pros
Marno Verbeek Erasmus University, the Netherlands Using linear regression to establish empirical relationships Linear regression is a powerful tool for estimating the relationship between one variable
More informationInference with DifferenceinDifferences Revisited
Inference with DifferenceinDifferences Revisited M. Brewer, T F. Crossley and R. Joyce Journal of Econometric Methods, 2018 presented by Federico Curci February 22nd, 2018 Brewer, Crossley and Joyce
More informationChapter 11 Nonexperimental Quantitative Research Steps in Nonexperimental Research
Chapter 11 Nonexperimental Quantitative Research (Reminder: Don t forget to utilize the concept maps and study questions as you study this and the other chapters.) Nonexperimental research is needed because
More informationCorrelation and Regression
Dublin Institute of Technology ARROW@DIT Books/Book Chapters School of Management 201210 Correlation and Regression Donal O'Brien Dublin Institute of Technology, donal.obrien@dit.ie Pamela Sharkey Scott
More informationEmpirical Strategies
Empirical Strategies Joshua Angrist BGPE March 2012 These lectures cover many of the empirical modeling strategies discussed in Mostly Harmless Econometrics (MHE). The main theoretical ideas are illustrated
More informationMULTIPLE REGRESSION OF CPS DATA
MULTIPLE REGRESSION OF CPS DATA A further inspection of the relationship between hourly wages and education level can show whether other factors, such as gender and work experience, influence wages. Linear
More informationClass 1: Introduction, Causality, Selfselection Bias, Regression
Class 1: Introduction, Causality, Selfselection Bias, Regression Ricardo A Pasquini April 2011 Ricardo A Pasquini () April 2011 1 / 23 Introduction I Angrist s what should be the FAQs of a researcher:
More informationPropensity Score Methods for Estimating Causality in the Absence of Random Assignment: Applications for Child Care Policy Research
2012 CCPRC Meeting Methodology Presession Workshop October 23, 2012, 2:005:00 p.m. Propensity Score Methods for Estimating Causality in the Absence of Random Assignment: Applications for Child Care Policy
More informationGlossary From Running Randomized Evaluations: A Practical Guide, by Rachel Glennerster and Kudzai Takavarasha
Glossary From Running Randomized Evaluations: A Practical Guide, by Rachel Glennerster and Kudzai Takavarasha attrition: When data are missing because we are unable to measure the outcomes of some of the
More informationThe Issue of Endogeneity within TheoryBased, Quantitative Management Accounting Research
European Accounting Review Vol. 16, No. 1, 173 195, 2007 The Issue of Endogeneity within TheoryBased, Quantitative Management Accounting Research ROBERT H. CHENHALL, & FRANK MOERS Monash University, James
More informationRandomized Evaluations
Randomized Evaluations Introduction, Methodology, & Basic Econometrics using Mexico s Progresa program as a case study (with thanks to Clair Null, author of 2008 Notes) Sept. 15, 2009 Not All Correlations
More informationCausal Validity Considerations for Including High Quality NonExperimental Evidence in Systematic Reviews
NonExperimental Evidence in Systematic Reviews OPRE REPORT #201863 DEKE, MATHEMATICA POLICY RESEARCH JUNE 2018 OVERVIEW Federally funded systematic reviews of research evidence play a central role in
More informationReading and maths skills at age 10 and earnings in later life: a brief analysis using the British Cohort Study
Reading and maths skills at age 10 and earnings in later life: a brief analysis using the British Cohort Study CAYT Impact Study: REP03 Claire Crawford Jonathan Cribb The Centre for Analysis of Youth Transitions
More informationEc331: Research in Applied Economics Spring term, Panel Data: brief outlines
Ec331: Research in Applied Economics Spring term, 2014 Panel Data: brief outlines Remaining structure Final Presentations (5%) Fridays, 910 in H3.45. 15 mins, 8 slides maximum Wk.6 Labour Supply  Wilfred
More informationIntroduction to Quantitative Research and Program Evaluation Methods
Introduction to Quantitative Research and Program Evaluation Methods Dennis A. Kramer II, PhD Assistant Professor of Education Policy Director, Education Policy Research Center Agenda for the Day Brief
More informationResults & Statistics: Description and Correlation. I. Scales of Measurement A Review
Results & Statistics: Description and Correlation The description and presentation of results involves a number of topics. These include scales of measurement, descriptive statistics used to summarize
More informationSAMPLING AND SAMPLE SIZE
SAMPLING AND SAMPLE SIZE Andrew Zeitlin Georgetown University and IGC Rwanda With slides from Ben Olken and the World Bank s Development Impact Evaluation Initiative 2 Review We want to learn how a program
More informationComparing Proportions between Two Independent Populations. John McGready Johns Hopkins University
This work is licensed under a Creative Commons AttributionNonCommercialShareAlike License. Your use of this material constitutes acceptance of that license and the conditions of use of materials on this
More informationWELCOME! Lecture 11 Thommy Perlinger
Quantitative Methods II WELCOME! Lecture 11 Thommy Perlinger Regression based on violated assumptions If any of the assumptions are violated, potential inaccuracies may be present in the estimated regression
More information26:010:557 / 26:620:557 Social Science Research Methods
26:010:557 / 26:620:557 Social Science Research Methods Dr. Peter R. Gillett Associate Professor Department of Accounting & Information Systems Rutgers Business School Newark & New Brunswick 1 Overview
More informationOn the purpose of testing:
Why Evaluation & Assessment is Important Feedback to students Feedback to teachers Information to parents Information for selection and certification Information for accountability Incentives to increase
More informationLecture 2: Learning and Equilibrium ExtensiveForm Games
Lecture 2: Learning and Equilibrium ExtensiveForm Games III. Nash Equilibrium in Extensive Form Games IV. SelfConfirming Equilibrium and Passive Learning V. Learning Offpath Play D. Fudenberg Marshall
More informationCausality and Statistical Learning
Department of Statistics and Department of Political Science, Columbia University 27 Mar 2013 1. Different questions, different approaches Forward causal inference: What might happen if we do X? Effects
More informationThe Dynamic Effects of Obesity on the Wages of Young Workers
The Dynamic Effects of Obesity on the Wages of Young Workers Joshua C. Pinkston University of Louisville June, 2015 Contributions 1. Focus on more recent cohort, NLSY97. Obesity
More informationMultivariable Systems. Lawrence Hubert. July 31, 2011
Multivariable July 31, 2011 Whenever results are presented within a multivariate context, it is important to remember that there is a system present among the variables, and this has a number of implications
More informationCausal Methods for Observational Data Amanda Stevenson, University of Texas at Austin Population Research Center, Austin, TX
Causal Methods for Observational Data Amanda Stevenson, University of Texas at Austin Population Research Center, Austin, TX ABSTRACT Comparative effectiveness research often uses nonexperimental observational
More informationPreliminary Draft. The Effect of Exercise on Earnings: Evidence from the NLSY
Preliminary Draft The Effect of Exercise on Earnings: Evidence from the NLSY Vasilios D. Kosteas Cleveland State University 2121 Euclid Avenue, RT 1707 Cleveland, OH 441152214 b.kosteas@csuohio.edu Tel:
More informationVersion No. 7 Date: July Please send comments or suggestions on this glossary to
Impact Evaluation Glossary Version No. 7 Date: July 2012 Please send comments or suggestions on this glossary to 3ie@3ieimpact.org. Recommended citation: 3ie (2012) 3ie impact evaluation glossary. International
More informationSimple Linear Regression One Categorical Independent Variable with Several Categories
Simple Linear Regression One Categorical Independent Variable with Several Categories Does ethnicity influence total GCSE score? We ve learned that variables with just two categories are called binary
More informationStudy of cigarette sales in the United States Ge Cheng1, a,
2nd International Conference on Economics, Management Engineering and Education Technology (ICEMEET 2016) 1Department Study of cigarette sales in the United States Ge Cheng1, a, of pure mathematics and
More informationDylan Small Department of Statistics, Wharton School, University of Pennsylvania. Based on joint work with Paul Rosenbaum
Instrumental variables and their sensitivity to unobserved biases Dylan Small Department of Statistics, Wharton School, University of Pennsylvania Based on joint work with Paul Rosenbaum Overview Instrumental
More informationThe Effects of Smoking in Young Adulthood on. the Vietnam Era Draft Lottery
The Effects of Smoking in Young Adulthood on Smoking and Health Later in Life: Evidence based on the Vietnam Era Draft Lottery August 2008 Abstract An important, unresolved question for health policymakers
More informationSection 3.2 LeastSquares Regression
Section 3.2 LeastSquares Regression Linear relationships between two quantitative variables are pretty common and easy to understand. Correlation measures the direction and strength of these relationships.
More informationWRITTEN PRELIMINARY Ph.D. EXAMINATION. Department of Applied Economics. January 17, Consumer Behavior and Household Economics.
WRITTEN PRELIMINARY Ph.D. EXAMINATION Department of Applied Economics January 17, 2012 Consumer Behavior and Household Economics Instructions Identify yourself by your code letter, not your name, on each
More informationCHAPTER 2: TWOVARIABLE REGRESSION ANALYSIS: SOME BASIC IDEAS
CHAPTER 2: TWOVARIABLE REGRESSION ANALYSIS: SOME BASIC IDEAS 2.1 It tells how the mean or average response of the subpopulations of Y varies with the fixed values of the explanatory variable (s). 2.2
More informationEmpowered by Psychometrics The Fundamentals of Psychometrics. Jim Wollack University of Wisconsin Madison
Empowered by Psychometrics The Fundamentals of Psychometrics Jim Wollack University of Wisconsin Madison Psychowhat? Psychometrics is the field of study concerned with the measurement of mental and psychological
More informationMotherhood and Female Labor Force Participation: Evidence from Infertility Shocks
Motherhood and Female Labor Force Participation: Evidence from Infertility Shocks Jorge M. Agüero Univ. of California, Riverside jorge.aguero@ucr.edu Mindy S. Marks Univ. of California, Riverside mindy.marks@ucr.edu
More informationECON Microeconomics III
ECON 7130  Microeconomics III Spring 2016 Notes for Lecture #5 Today: DifferenceinDifferences (DD) Estimators DifferenceinDifferenceinDifferences (DDD) Estimators (Triple Difference) DifferenceinDifference
More informationThe Late Pretest Problem in Randomized Control Trials of Education Interventions
The Late Pretest Problem in Randomized Control Trials of Education Interventions Peter Z. Schochet ACF Methods Conference, September 2012 In Journal of Educational and Behavioral Statistics, August 2010,
More informationCausality and Statistical Learning
Department of Statistics and Department of Political Science, Columbia University 29 Sept 2012 1. Different questions, different approaches Forward causal inference: What might happen if we do X? Effects
More informationIntroduction to instrumental variables and their application to large scale assessment data
DOI 10.1186/s4053601600182 RESEARCH Open Access Introduction to instrumental variables and their application to large scale assessment data Artur Pokropek * *Correspondence: artur.pokropek@gmail.com
More informationCausal Inference in Statistics A primer, J. Pearl, M Glymur and N. Jewell
Causal Inference in Statistics A primer, J. Pearl, M Glymur and N. Jewell Rina Dechter Bren School of Information and Computer Sciences 6/7/2018 dechter, class 8, 27618 The book of Why Pearl https://www.nytimes.com/2018/06/01/business/dealbook/revi
More informationF1: Introduction to Econometrics
F1: Introduction to Econometrics Feng Li Department of Statistics, Stockholm University General information Homepage of this course: http://gauss.stat.su.se/gu/ekonometri.shtml Lecturer F1 F7: Feng Li,
More information6. Unusual and Influential Data
Sociology 740 John ox Lecture Notes 6. Unusual and Influential Data Copyright 2014 by John ox Unusual and Influential Data 1 1. Introduction I Linear statistical models make strong assumptions about the
More informationPrediction, Causation, and Interpretation in Social Science. Duncan Watts Microsoft Research
Prediction, Causation, and Interpretation in Social Science Duncan Watts Microsoft Research Explanation in Social Science: Causation or Interpretation? When social scientists talk about explanation they
More informationEconometric analysis and counterfactual studies in the context of IA practices
Econometric analysis and counterfactual studies in the context of IA practices Giulia Santangelo http://crie.jrc.ec.europa.eu Centre for Research on Impact Evaluation DG EMPL  DG JRC CRIE Centre for Research
More informationLecture 4: Research Approaches
Lecture 4: Research Approaches Lecture Objectives Theories in research Research design approaches ú Experimental vs. nonexperimental ú Crosssectional and longitudinal ú Descriptive approaches How to
More informationStructural Equation Modeling (SEM)
Structural Equation Modeling (SEM) Today s topics The Big Picture of SEM What to do (and what NOT to do) when SEM breaks for you Single indicator (ASU) models Parceling indicators Using single factor scores
More informationDoes Male Education Affect Fertility? Evidence from Mali
Does Male Education Affect Fertility? Evidence from Mali Raphael Godefroy (University of Montreal) Joshua Lewis (University of Montreal) April 6, 2018 Abstract This paper studies how school access affects
More informationMidterm STATUB.0003 Regression and Forecasting Models. I will not lie, cheat or steal to gain an academic advantage, or tolerate those who do.
Midterm STATUB.0003 Regression and Forecasting Models The exam is closed book and notes, with the following exception: you are allowed to bring one lettersized page of notes into the exam (front and
More informationFinal Research on Underage Cigarette Consumption
Final Research on Underage Cigarette Consumption Angie Qin An Hu New York University Abstract Over decades, we witness a significant increase in amount of research on cigarette consumption. Among these
More informationThe Effects of Maternal Alcohol Use and Smoking on Children s Mental Health: Evidence from the National Longitudinal Survey of Children and Youth
1 The Effects of Maternal Alcohol Use and Smoking on Children s Mental Health: Evidence from the National Longitudinal Survey of Children and Youth Madeleine Benjamin, MA Policy Research, Economics and
More informationEXERCISE: HOW TO DO POWER CALCULATIONS IN OPTIMAL DESIGN SOFTWARE
...... EXERCISE: HOW TO DO POWER CALCULATIONS IN OPTIMAL DESIGN SOFTWARE TABLE OF CONTENTS 73TKey Vocabulary37T... 1 73TIntroduction37T... 73TUsing the Optimal Design Software37T... 73TEstimating Sample
More informationThe Logic of Causal Order Richard Williams, University of Notre Dame, https://www3.nd.edu/~rwilliam/ Last revised February 15, 2015
The Logic of Causal Order Richard Williams, University of Notre Dame, https://www3.nd.edu/~rwilliam/ Last revised February 15, 2015 [NOTE: Toolbook files will be used when presenting this material] First,
More information12.1 Inference for Linear Regression. Introduction
12.1 Inference for Linear Regression vocab examples Introduction Many people believe that students learn better if they sit closer to the front of the classroom. Does sitting closer cause higher achievement,
More informationSTATISTICAL METHODS FOR DIAGNOSTIC TESTING: AN ILLUSTRATION USING A NEW METHOD FOR CANCER DETECTION XIN SUN. PhD, Kansas State University, 2012
STATISTICAL METHODS FOR DIAGNOSTIC TESTING: AN ILLUSTRATION USING A NEW METHOD FOR CANCER DETECTION by XIN SUN PhD, Kansas State University, 2012 A THESIS Submitted in partial fulfillment of the requirements
More informationUsing ASPES (Analysis of Symmetrically Predicted Endogenous Subgroups) to understand variation in program impacts. Laura R. Peck.
Using ASPES (Analysis of Symmetrically Predicted Endogenous Subgroups) to understand variation in program impacts Presented by: Laura R. Peck OPRE Methods Meeting on What Works Washington, DC September
More informationAre Illegal Drugs Inferior Goods?
Are Illegal Drugs Inferior Goods? Suryadipta Roy West Virginia University This version: January 31, 2005 Abstract Using data from the National Survey on Drug Use and Health, evidence of income inferiority
More informationQuasiexperimental analysis Notes for "Structural modelling".
Quasiexperimental analysis Notes for "Structural modelling". Martin Browning Department of Economics, University of Oxford Revised, February 3 2012 1 Quasiexperimental analysis. 1.1 Modelling using quasiexperiments.
More information