Instrumental Variables & 2SLS

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1 Instrumental Variables & 2SLS y 1 = β 0 + β 1 y 2 + β 2 z β k z k + u y 2 = π 0 + π 1 z k+1 + π 2 z π k z k + v Economics 20 - Prof. Schuetze 1 Why Use Instrumental Variables? Instrumental Variables (IV) estimation i is used when your model has endogenous x s i.e. whenever Cov(x,u) 0 Thus, IV can be used to address the problem of omitted variable bias Also, IV can be used to solve the classic errors-invariables problem Economics 20 - Prof. Schuetze 2 1

2 What Is an Instrumental Variable? In order for a variable, z, to serve as a valid instrument for x, the following must be true 1. The instrument must be exogenous 2. The instrument must be correlated with the endogenous variable x Economics 20 - Prof. Schuetze 3 Difference between IV and Proxy? With IV we will leave the unobserved variable in the error term but use an estimation method that recognizes the presence of the omitted variable With a proxy we were trying to remove the unobserved variable from the error term e.g. IQ Economics 20 - Prof. Schuetze 4 2

3 More on Valid Instruments We can t test if Cov(z,u) = 0 as this is a population assumption Instead, we have to rely on common sense and economic theory to decide if it makes sense However, we can test if Cov(z,x) 0 using a random sample Simply estimate x = π 0 + π 1 z + v, and test H 0 : π 1 = 0 Economics 20 - Prof. Schuetze 5 IV Estimation in the Simple Regression Case We can show that if we have a valid instrument we can get consistent t estimates t of the parameters For y = β 0 + β 1 x + u Cov(z,y) = β 1 Cov(z,x) + Cov(z,u), so Thus, β 1 = Cov(z,y) / Cov(z,x), since Cov(z,u)=0 and Cov(z,x) 0 Then the IV estimator t for β 1 is Economics 20 - Prof. Schuetze 6 3

4 Inference with IV Estimation The IV estimator also has an approximate normal distribution in large samples To get estimates of the standard errors we need a slightly different homoskedasticity assumption: If this is true, we can show that the asymptotic variance of β 1 -hat is: Economics 20 - Prof. Schuetze 7 Inference with IV Estimation Each of the elements in the population variance can be estimated t from a random sample The estimated variance is then: 2 ( ˆ ˆ σ Var β 1) = SST R 2 x x, z Economics 20 - Prof. Schuetze 8 4

5 IV versus OLS estimation Standard error in IV case differs from OLS only in the R 2 from regressing x on z Since R 2 < 1, IV standard errors are larger Economics 20 - Prof. Schuetze 9 The Effect of Poor Instruments What if our assumption that Cov(z,u) = 0 is false? The IV estimator will be inconsistent also We can compare the asymptotic bias in OLS to that in IV in this case: Corr( z, u) σ u IV : plim ˆ β 1 = β1 + Corr( z, x) σ ~ OLS: plim β 1 = β1 + Corr( x, u) x σ σ u x Economics 20 - Prof. Schuetze 10 5

6 Effect of Poor Instruments (cont) So, it is not necessarily better to us IV instead of OLS even if z and u are not highly correlated Also notice that the inconsistency gets really large if z and x are only loosely correlated Economics 20 - Prof. Schuetze 11 A Note on R 2 in IV R 2 after IV estimation can be negative Recall that R 2 = 1 - SSR/SST where SSR is the residual sum of IV residuals SSR in this case can be larger than SST making the R 2 negative Thus, R 2 isn t very useful here and can tbeused for F-tests Economics 20 - Prof. Schuetze 12 6

7 IV Estimation in the Multiple Regression Case IV estimation can be extended to the multiple regression case Estimating: y 1 = β 0 + β 1 y 2 + β 2 z 1 + u 1 Where y 2 is endogenous and z 1 is exogenous Call this the structural model If we estimate the structural model the coefficients will be biased and inconsistent i t Thus, we need an instrument for y 2 Economics 20 - Prof. Schuetze 13 Multiple Regression IV (cont) No, because it appears in the structural model Instead, we need an instrument, z 2, that: Now because of z 1 we need a partial correlation - i.e. for the reduced form equation y 2 = π 0 + π 1 z 1 + π 2 z 2 + v 2 Economics 20 - Prof. Schuetze 14 7

8 Two Stage Least Squares (2SLS) It is possible to have multiple instruments Consider the structural model, with 1 endogenous, y 2, and 1 exogenous, z 1, RHS variable Suppose that we have two valid instruments, z 2 and z 3 Since z 1, z 2 and z 3 are uncorrelated with u 1, so is any linear combination of these Thus, any linear combination is also a valid instrument Economics 20 - Prof. Schuetze 15 Best Instrument The best instrument is the one that is most highly correlated ltdwith y 2 This turns out to be a linear combination of the exogenous variables The reduce form equation is: y 2 = π 0 + π 1 z 1 + π 2 z 2 + π 3 z 3 + v 2 or y 2 = y 2 * + v 2 Economics 20 - Prof. Schuetze 16 8

9 More on 2SLS We can estimate y 2 * by regressing gyy 2 on z 1, z 2 and z 3 the first stage regression If then substitute ŷ 2 for y 2 in the structural model, get same coefficient as IV While the coefficients are the same, the standard errors from doing 2SLS by hand are incorrect Also recall that since the R2 can be negative F- tests will be invalid Stata will calculate the correct standard error and F-tests Economics 20 - Prof. Schuetze 17 More on 2SLS (cont) We can extend this method to include multiple endogenous variables Economics 20 - Prof. Schuetze 18 9

10 Addressing Errors-in-Variables with IV Estimation Recall the classical errors-in-variables problem where we observe x 1 instead of x 1 * Where x 1 = x 1 * + e 1, we showed that when x 1 and e 1 are correlated the OLS estimates are biased We maintain the assumption that u is uncorrelated with x 1 *, x 1 and x 2 and that and e 1 is uncorrelated with x 1 * and x 2 Economics 20 - Prof. Schuetze 19 Example of Instrument Suppose that we have a second measure of x 1 *(z 1 ) Examples: z 1 will also measure x 1 * with error However, as long as the measurement error in z 1 is uncorrelated with the measurement error in x 1, z 1 is a valid instrument Economics 20 - Prof. Schuetze 20 10

11 Testing for Endogeneity Since OLS is preferred to IV if we do not have an endogeneity problem, then we d like to be able to test for endogeneity Suppose we have the following structural model: y 1 = β 0 + β 1 y 2 + β 2 z 1 + β 3 z 2 + u We suspect that y 2 is endogenous and we have instruments for y 2 (z 3, z 4 ) Economics 20 - Prof. Schuetze 21 Testing for Endogeneity (cont) 1. Hausman Test If all variables are exogenous both OLS and 2SLS are consistent 2R 2. Regression Test In the first stage equation: y 2 = π 0 + π 1 z 1 + π 2 z 2 + π 3 z 3 + π 3 z 3 + v 2 Each of the z s are uncorrelated with u 1 Economics 20 - Prof. Schuetze 22 11

12 Testing for Endogeneity (cont) So, save the residuals from the first stage Include the residual in the structural equation (which of course has y 2 in it) If the coefficient on the residual is statistically different from zero, reject the null of exogeneity Economics 20 - Prof. Schuetze 23 Testing Overidentifying Restrictions How can we determine if we have a good instrument -correlated with y 2 uncorrelated with u? Easy to test if z is correlated with y 2 If there is just one instrument for our endogenous variable, we can t test whether the instrument is uncorrelated with the error (u is unobserved) If we have multiple l instruments, it is possible to test the overidentifying restrictions Economics 20 - Prof. Schuetze 24 12

13 The OverID Test Using our previous example, suppose we have two instruments for y 2 (z 3, z 4 ) We could estimate our structural model using only z 3 as an instrument, assuming it is uncorrelated with the error, and get the residuals: Economics 20 - Prof. Schuetze 25 The OverID Test We could do the same for z 3, as long as we can assume that z 4 is uncorrelated with u 1 A procedure that allows us to do this is: Under the null that all instruments are uncorrelated with the error, LM ~ χ q2 where q is the number of extra instruments Economics 20 - Prof. Schuetze 26 13

14 Testing for Heteroskedasticity When using 2SLS, we need a slight adjustment to the Breusch-Pagan test Get the residuals from the IV estimation Regress these residuals squared on all of the exogenous variables in the model (including the instruments) Test for the joint significance Note: there are also robust standard errors in the IV setting Economics 20 - Prof. Schuetze 27 Testing for Serial Correlation Also need a slight adjustment to the test for serial correlation when using 2SLS Re-estimate the structural model by 2SLS, including the lagged residuals, and using the same instruments as originally Test if the coefficient on the lagged residual (ρ) is statistically different than zero Can also correct for serial correlation by doing 2SLS on a quasi-differenced model, using quasidifferenced instruments Economics 20 - Prof. Schuetze 28 14

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