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Instrumental Variables I. Objective We are trying to learn the effect of education on income We have Card (1993)’s data on years of schooling, wages,

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Presentation on theme: "Instrumental Variables I. Objective We are trying to learn the effect of education on income We have Card (1993)’s data on years of schooling, wages,"— Presentation transcript:

1 Instrumental Variables I

2 Objective We are trying to learn the effect of education on income We have Card (1993)’s data on years of schooling, wages, proximity to a four year college and various other controls. We will obtain OLS and IV estimates of the returns to education and discuss any problems in this particular context and in general

3 OLS Results. reg lwage educ exper expersq black smsa smsa66 south reg66*, robust Linear regression Number of obs = 3010 F( 15, 2994) = Prob > F = R-squared = Root MSE = | Robust lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] educ | exper | expersq | black | smsa | smsa66 | south | reg661 | reg662 | …… Are you surprised? What is the OLS Identification Assumption? What sources of bias are likely to be present? Which direction are these sources of bias likely to bias our estimates?

4 What do we require for an instrument to be valid?

5 1.Relevance: cov(z, x) ≠ 0 2.Exogeneity cov(z, e) = 0

6 What do we require for an instrument to be valid? 1.Relevance: cov(z, x) ≠ 0 – Important because if the instrument isn’t correlated with the endogenous variable then knowing the value of the instrument doesn’t tell us anything about the endogenous variable. – Do we care about the unconditional correlation or the correlation conditional on the other controls? Why? – Can we test this? How? 2.Exogeneity cov(z, e) = 0

7 What do we require for an instrument to be valid? 1.Relevance: cov(z, x) ≠ 0 2.Exogeneity cov(z, e) = 0 – Important because we want the instrument to effect z only through x – Can we test this? If not what do we do instead? – How does this assumption relate to the key OLS identification assumption?

8 Testing Relevance How can we test the relevance of an instrument?

9 Testing Relevance How can we test the relevance of an instrument? 1.Calculate cor(x,z) – Better than nothing but not ideal. Why? 2.Run the ‘first stage’ regression – What should we include? – What do we look at? – What if we have more than one instrument? – What if we have more than one endogenous variable? 3.Use the post-estimation commands after estimating our main regression. We’ll do (2) today.

10 1 st Stage Results reg educ nearc4 exper expersq black smsa smsa66 south reg66*, robust note: reg666 omitted because of collinearity Linear regression Number of obs = 3010 F( 15, 2994) = Prob > F = R-squared = Root MSE = | Robust educ | Coef. Std. Err. t P>|t| [95% Conf. Interval] nearc4 | exper | expersq | Where do we look to test the Relevance condition? Is it satisfied?

11 First-Stage F A ‘First Stage F-Statistic’ in excess of 10 is often used as the threshold for satisfaction of the Relevance condition What do we mean by a first stage F Statistic Can we see it on the previous slide? – (we can, but not directly) in general you can use Stata’s ‘test’ command

12 How plausible is it that nearc4 is exogenous?

13 IV Results ivregress 2sls lwage (educ=nearc4) exper expersq black smsa smsa66 south reg66*, robust note: reg669 omitted because of collinearity Instrumental variables (2SLS) regression Number of obs = 3010 Wald chi2(15) = Prob > chi2 = R-squared = Root MSE = | Robust lwage | Coef. Std. Err. z P>|z| [95% Conf. Interval] educ | exper | expersq | black | smsa | smsa66 | south | reg661 | How have the results changed? Are they what you expect? What explanations could there be for the differences?

14 Does the exclusion of IQ break the exogeneity condition?. reg IQ nearc4 Source | SS df MS Number of obs = F( 1, 2059) = Model | Prob > F = Residual | R-squared = Adj R-squared = Total | Root MSE = IQ | Coef. Std. Err. t P>|t| [95% Conf. Interval] nearc4 | _cons |

15 How about now?. reg IQ nearc4 smsa66 reg662-reg669 Source | SS df MS Number of obs = F( 10, 2050) = Model | Prob > F = Residual | R-squared = Adj R-squared = Total | Root MSE = IQ | Coef. Std. Err. t P>|t| [95% Conf. Interval] nearc4 | smsa66 | reg662 | reg663 | reg664 | reg665 | reg666 | reg667 | reg668 | reg669 | _cons |

16 Do we believe the IV results?


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