Report

Review #1 … 1 Is there a systematic way to assess regression studies? (SW Chapter 9) Multiple regression has some key virtues: · It provides an estimate of the marginal effect of X on Y · It resolves the problem of omitted variable bias, if an omitted variable can be measured and included · It can handle nonlinear effects Still, OLS might yield a biased estimator of the true causal effect – it might not yield valid inferences… 2 A Framework for Assessing Statistical Studies: Internal and External Validity 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 · “setting” refers to the legal, institutional, policy, physical environment and even time frame 3 Threats to External Validity How far can we generalize class size results from California school districts? · Differences in populations · California in 2005? · Massachusetts in 2005? · Mexico in 2005? · Differences in settings · different legal requirements concerning special education · different treatment of bilingual education · differences in teacher characteristics 4 Threats to Internal Validity 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. Wrong functional form 2. Omitted variable bias 3. Errors-in-variables bias 4. Sample selection bias 5. Simultaneous causality bias · All of these imply what? · And the consequence is what? 5 1. Wrong functional form Arises if the functional form is incorrect – for example, an interaction term is incorrectly omitted or quadratic term is incorrectly used instead of log – then inferences on causal effects will be biased!! Potential solutions to functional form misspecification 1. If Y is continuous, use the “appropriate” nonlinear specifications in X 2. If Y is discrete, move to “probit” or “logit” analysis 6 2. Omitted variable bias Omitted variable bias arises if an omitted variable is both: (i) a determinant of Y and (ii) correlated with at least one included regressor. Potential Solutions: 1. Measure & include the omitted var 2. Use panel data 3. Use instrumental variables regression 4. Conduct an RCT Why does this work? 7 3. Errors-in-variables bias So far we’ve assumed X is perfectly measured … · Data entry errors · Recollection errors in surveys (when did you start your job?) · Ambiguous questions problems (income last year?) · Intentionally false response problems with surveys o What is the current value of your financial assets? o How often do you drink and drive? o Have you ever cheated at Davidson? 8 Errors-in-variables bias Suppose LSA #1 - #3 hold, Yi = b0 + b1Xi + ui Let Xi = unmeasured true value of X X! = imprecisely measured version of X i 9 10 Errors-in-variables bias Yi = b0 + b1 X!i + u!i , where u!i = b1(Xi – X!i ) + ui · If Xi is measured with error, X!i is in general correlated with u!i , so bˆ1 is biased and inconsistent! · Possible to derive formulas for the bias based on specific mathematical assumptions about the measurement error process … let’s look at one such case o Classical measurement error 11 Potential solutions to errors-in-variables bias 1. Obtain better data 2. Develop a model of the measurement error process · Need to know a lot about the nature of the measurement error – for example by cross-checking a subsample of the data against separate administrative records and analyzing the discrepancies, and creating a model of them · Very specialized; we won’t pursue this in this course 3. Instrumental variables regression 12 4. Sample selection bias So far we’ve assumed i.i.d. random sampling. Sometimes this is thwarted because the sample “selects itself” Sample selection bias arises when a selection process: (i) influences the availability of data and (ii) that process is related to the outcome 13 Example #1: Mutual funds · Do actively managed mutual funds outperform “hold-themarket” funds? · Empirical strategy: · Sampling scheme: i.i.d. random sampling of mutual funds available to the public on a given date · Data: returns for the preceding 10 years. · Estimator: average ten-year return of the sample mutual funds, minus ten-year return on S&P500 · Is there sample selection bias? § returni = b0 + b1managed_fundi + ui 14 Example #2: Returns to education 15 Potential solutions to sample selection bias · Collect the sample in a way that avoids sample selection. · Mutual funds example: change the sample population from those available at the end of the ten-year period, to those available at the beginning of the period (include failed funds) · Returns to education example: sample college graduates, not workers (include the unemployed) · RCT · Construct a model of the sample selection process and estimate that model (we won’t do this) 16 5. Simultaneous causality bias 17 Simultaneous causality bias 18 Potential solutions to simultaneous causality bias 1. RCT. Because Xi is chosen at random by the experimenter, there is no feedback from the outcome variable to Yi (assuming perfect compliance). 2. Develop and estimate a complete model of both directions of causality. This is the idea behind many large macro models (e.g. Federal Reserve Bank-US). This is extremely difficult in practice. 3. Use instrumental variables regression to estimate the causal effect of interest (effect of X on Y, ignoring effect of Y on X). 19 Internal and External Validity When the Regression is Used for Forecasting · Forecasting and estimation of causal effects are quite different objectives. · For forecasting, · R 2 matters (a lot!) · Omitted variable bias isn’t a problem! · Interpreting coefficients in forecasting models is not important – the important thing is a good fit and a model you can “trust” to work in your application · External validity is paramount: the model estimated using historical data must hold into the (near) future · More on forecasting when we take up time series data 20 Applying External and Internal Validity: Test Scores and Class Size Objective: Assess the threats to the internal and external validity of the empirical analysis of the California test score data. · External validity · Compare results for California and Massachusetts · Think carefully … · Internal validity · Go through the list of five potential threats to internal validity and think carefully … 21 The Massachusetts data: summary statistics 22 23 24 25 How do the Mass and California results compare? · How best to control for Income? · Evidence of nonlinearity in TestScore-STR relation? · Is there a significant HiEL*STR interaction? Income California ln(Inc) fit best Massachusetts cubic fits best STR cubic fit best linear fits best Different levels, but effects of STR similar for HiEL = 1&0 no HiEL*STR interaction? 26 Predicted effects for a class size reduction of 2 27 28 Summary of Findings for Massachusetts 29 Comparison of estimated class size effects: CA vs. MA 30 Summary: Comparison of California and Massachusetts Regression Analyses · Class size effect falls in both CA, MA data when student and district control variables are added. · Class size effect is statistically significant in both CA, MA data. · Estimated effect of a 2-student reduction in STR is quantitatively similar for CA, MA. · Neither data set shows evidence of STR – PctEL interaction. · Some evidence of STR nonlinearities in CA data, but not in MA data. 31 Remaining threats to internal validity in the test score/class size example? 1. Wrong functional form? 2. Omitted variable bias? 3. Errors-in-variables bias? 4. Selection bias? 5. Simultaneous causality bias? 32 Spot the Endogeneity? • Estimating the effect of police on crime … • Effect of police on crime … • Source: Levitt (American Economic Review, 1997) 33 Spot the Endogeneity? • Estimating the effect of police on crime … 34 Spot the Endogeneity? • Estimating the effect of guns on crime … • Source: Ayres and Donohue (Stanford Law Review, 2003) 35 36 37 38 39 Spot the Endogeneity? • Estimating the effect of studying on GPA … • Source: Stinebricker and Stinebricker (BE Journal of Analysis & Policy, 2008) 40 Spot the Endogeneity? • Estimating the effect of school start time on academic performance … • Source: Carrell, Maghakian, and West (American Economic Journal: Economic Policy, 2011) 41 42 Spot the Endogeneity? • Estimating the effect of national income on democracy … • Source: Acemoglu, Johnson, Robinson, and Yared (American Economic Review, 2008) 43 Spot the Endogeneity? • Estimating the effect of competition on student achievement … • Source: Hoxby (American Economic Review, 2000) 44 Spot the Endogeneity? • Estimating the effect of teen drinking on HS completion/attending college … • Source: Dee & Evans (Journal of Labor Economics, 2003) 45