Chapter 10

Chapter 10

Endogenous Regressors and Instrumental Variables Estimation

Adapted from Vera Tabakova, East Carolina University

Chapter 10: Random Regressors and Moment Based Estimation 10.1 Linear Regression with Random x’s 10.2 Cases in which x and e are Correlated 10.3 Estimators Based on the Method of Moments 10.4 Specification Tests

Slide 10-2Principles of Econometrics, 3rd Edition

Chapter 10: Random Regressors and Moment Based EstimationThe assumptions of the simple linear regression are: SR1. SR2. SR3. SR4.

SR5. The variable xi is not random, and it must take at least two

different values. SR6. (optional)


1 2 1, ,i i iy x e i N

( ) 0iE e 2var( )ie

cov( , ) 0i je e

2~ (0, )ie N

Chapter 10: Random Regressors and Moment Based Estimation

The purpose of this chapter is to discuss regression models in which

xi is random and correlated with the error term ei. We will:

Discuss the conditions under which having a random x is not a

problem, and how to test whether our data satisfies these conditions.

Present cases in which the randomness of x causes the least squares

estimator to fail.

Provide estimators that have good properties even when xi is random

and correlated with the error ei. Slide 10-4Principles of Econometrics, 3rd Edition

10.1 Linear Regression With Random X’s

A10.1 correctly describes the relationship

between yi and xi in the population, where β1 and β2 are unknown

(fixed) parameters and ei is an unobservable random error term.

A10.2 The data pairs , are obtained by random

sampling. That is, the data pairs are collected from the same

population, by a process in which each pair is independent of

every other pair. Such data are said to be independent and

identically distributed.


1 2i i iy x e

, 1, ,i ix y i N


A10.3 The expected value of the error term ei,

conditional on the value of xi, is zero.

This assumption implies that we have (i) omitted no important variables, (ii) used the correct functional form, and (iii) there exist no factors that cause the error term ei to be correlated with xi.

If , then we can show that it is also true that xi and ei are uncorrelated, and that .

Conversely, if xi and ei are correlated, then and we can show that .


| 0.i iE e x

| 0i iE e x cov , 0i ix e

cov , 0i ix e | 0i iE e x


A10.4 In the sample, xi must take at least two different values.

A10.5 The variance of the error term, conditional

on xi is a constant σ2.

A10.6 The distribution of the error term,

conditional on xi, is normal.


2var | .i ie x

2| ~ 0, .i ie x N

10.1.1 The Small Sample Properties of the OLS Estimator

Under assumptions A10.1-A10.4 the least squares estimator is

unbiased.

Under assumptions A10.1-A10.5 the least squares estimator is the

best linear unbiased estimator of the regression parameters,

conditional on the x’s, and the usual estimator of σ2 is unbiased.


10.1.1 The Small Sample Properties of the OLS Estimator

Under assumptions A10.1-A10.6 the distributions of the least squares

estimators, conditional upon the x’s, are normal, and their variances

are estimated in the usual way. Consequently the usual interval

estimation and hypothesis testing procedures are valid.


10.1.2 Asymptotic Properties of the OLS Estimator: X Not Random

Figure 10.1 An illustration of consistency


10.1.2 Asymptotic Properties of the OLS Estimator: X Not Random


Remark: Consistency is a “large sample” or “asymptotic” property. We have

stated another large sample property of the least squares estimators in Chapter

2.6. We found that even when the random errors in a regression model are not

normally distributed, the least squares estimators still have approximate

normal distributions if the sample size N is large enough. How large must the

sample size be for these large sample properties to be valid approximations of

reality? In a simple regression 50 observations might be enough. In multiple

regression models the number might be much higher, depending on the quality

of the data.

10.1.3 Asymptotic Properties of the OLS Estimator: X Random

A10.3*


0 and cov , 0i i iE e x e

| 0 cov , 0i i i iE e x x e

| 0 0i i iE e x E e


Under assumption A10.3* the least squares estimators are consistent.

That is, they converge to the true parameter values as N.

Under assumptions A10.1, A10.2, A10.3*, A10.4 and A10.5, the least

squares estimators have approximate normal distributions in large

samples, whether the errors are normally distributed or not.

Furthermore our usual interval estimators and test statistics are valid,

if the sample is large.



If assumption A10.3* is not true, and in particular if

so that xi and ei are correlated, then the least squares estimators are

inconsistent. They do not converge to the true parameter values

even in very large samples. Furthermore, none of our usual

hypothesis testing or interval estimation procedures are valid.


cov , 0i ix e

10.1.4 Why OLS Fails

Figure 10.2 Plot of correlated x and e




1 2 1 1y E y e x e x e

1 2ˆ .9789 1.7034y b b x x True model, but it gets estimated as:

…so there is s substantial bias …


Figure 10.3 Plot of data, true and fitted regressions


In the caseof a positivecorrelationbetween x and the error

ExampleWages = f(intelligence)

10.2 If X and e Are Correlated

When an explanatory variable and the error term are correlated the

explanatory variable is said to be endogenous and means

“determined within the system.”


10.2.1 Measurement Error


(10.1)

(10.2)

*1 2i i iy x v

*i i ix x u

X Y

vu

eX Y



(10.3)

*1 2

1 2

1 2 2

1 2

i i i

i i i

i i i

i i

y x v

x u v

x v u

x e



(10.4)

*2

2 22 2

cov ,

0

i i i i i i i i

i u

x e E x e E x u v u

E u

10.2.2 Omitted Variables

Omitted factors: experience, ability and motivation.

Therefore, we expect that


(10.5)1 2i i iWAGE EDUC e

cov , 0.i iEDUC e

We will focus on this case

Y

W

X

10.2.2 Omitted Variables

Other examples: Y = crime, X = marriage, W = “marriageability” Y = divorce, X = “shacking up”, W = “good match” Y = crime, X = watching a lot of TV, W = “parental involvement” Y = sexual violence, X = watching porn, W = any unobserved anything

that would affect both W and Y The list is endless!

.


We will focus on this case

Y

W

X

10.2.3 Simultaneous Equations Bias

There is a feedback relationship between Pi and Qi. Because of this

feedback, which results because price and quantity are jointly, or

simultaneously, determined, we can show that

The resulting bias (and inconsistency) is called the simultaneous

equations bias.


(10.6)1 2i i iQ P e

cov( , ) 0.i iP e

X Y

10.2.4 Lagged Dependent Variable Models with Serial Correlation

In this case the least squares estimator applied to the lagged

dependent variable model will be biased and inconsistent.


1 2 1 3t t t ty y x e

1AR(1) process: t t te e v

1If 0 there will be correlation between and .t ty e

10.3 Estimators Based on the Method of Moments

When all the usual assumptions of the linear model hold, the method

of moments leads us to the least squares estimator. If x is random and

correlated with the error term, the method of moments leads us to an

alternative, called instrumental variables estimation, or two-stage

least squares estimation, that will work in large samples.


10.3.3 Instrumental Variables Estimation in the Simple Linear Regression Model

Suppose that there is another variable, z, such that z does not have a direct effect on y, and thus it does not belong on the right-

hand side of the model as an explanatory variable ONCE X IS IN THE

MODEL!

zi should not be simultaneously affected by y either, of course

zi is not correlated with the regression error term ei. Variables with this

property are said to be exogenous z is strongly [or at least not weakly] correlated with x, the endogenous

explanatory variable A variable z with these properties is called an instrumental variable. Slide 10-27Principles of Econometrics, 3rd Edition


An instrument is a variable z correlated with x but not with the error e In addition, the instrument does not directly affect y and thus does not belong

in the actual model as a separate regressor (of course it should affect it through

the instrumented regressor x) It is common to have more than one instrument for x (just not good ones!) These instruments, z1; z2; : : : ; zs, must be correlated with x, but not with e Consistent estimation is obtained through the instrumental variables or two-

stage least squares (2SLS) estimator, rather than the usual OLS estimator


Instrumental Variables

Using Z, an “instrumental variable” for X is one solution to the problem of omitted variables bias

Z, to be a valid instrument for X must be:– Relevant = Correlated with X– Exogenous = Not correlated with Y

except through its correlation with X

Z

X Y

W

e



(10.16)

(10.17)

1 20 0i i i i iE z e E z y x

1 2

1 2

1 ˆ ˆ 0

1 ˆ ˆ 0

i i

i i i

y xN

z y xN

Based on the method of moments…



(10.18)

2

1 2

ˆ

ˆ ˆ

i ii i i i

i i i i i i

z z y yN z y z yN z x z x z z x x

y x

Solving the previous system, we obtain a new estimator that cleanses the endogeneity of X and exploits only the componentof the variation of X that is not correlated with e instead:

We do that by ensuring that we only use the predicted value of X from its regression on Z in the main regression


These new estimators have the following properties: They are consistent, if In large samples the instrumental variable estimators have

approximate normal distributions. In the simple regression model


(10.19)

0.i iE z e

2

2 2 22ˆ ~ ,

zx i

Nr x x


The error variance is estimated using the estimator


2

1 22ˆ ˆ

ˆ2

i i

IV

y x

N

(10.19)

2

2 2 22ˆ ~ ,

zx i

Nr x x

For:

The stronger the correlation between the instrument and X the better!

10.3.3a The importance of using strong instruments

Using the instrumental variables is less efficient than OLS (because

you must throw away some information on the variation of X) so it

leads to wider confidence intervals and less precise inference.

The bottom line is that when instruments are weak, instrumental

variables estimation is not reliable: you throw away too much

information when trying to avoid the endogeneity biasSlide 10-34Principles of Econometrics, 3rd Edition

2

22 2 22

varˆvarzxzx i

brr x x

Instrumental Variables using Venn diagrams

Not all of the available variation in X is used Only that component of X that is “explained”

by Z is used to explain Y

X Y

ZX = Endogenous variableY = Response variableZ = Instrumental variable

X Y

Z

Realistic scenario: Very little of X is explained by Z, and/or what is explained does not overlap much with Y

X YZ

Best-case scenario: A lot of X is explained by Z, and most of the overlap between X and Y is accounted for

Weak versus Strong IVs

Instrumental Variables Models

The IV estimator is BIASED E(bIV) ≠ β (finite-sample bias) but consistent: E(b) → β as N → ∞

So IV studies must often have very large samples But with endogeneity, E(bLS) ≠ β and plim(bLS) ≠ β anyway…

Asymptotic behavior of IV:plim(bIV) = β + Cov(Z,e) / Cov(Z,X)

If Z is truly exogenous, then Cov(Z,e) = 0

Some IV Terminology

Three different models to be familiar with

First stage (“Reduced form”): EDUC = α0 + α1Z + ω

Structural model: WAGES = β0 + β1EDUC + ε

WAGES = δ0 + δ1Z + ξ

An interesting equality:

δ1 = α1 × β1

so…

β1 = δ1 / α1

Z X Yα1 β1

Z Yδ1

ω ε

ξ

10.3.4a An Illustration Using Simulated Data


(10.26)

(10.27)

1 2ˆ .1947 .5700 .2068(se) (.079) (.089) (.077)

x z z

1 2_ ,ˆ 1.1376 1.0399

(se) (.116) (.194)IV z zy x

10.3.3b An Illustration Using Simulated Data


ˆ .9789 1.7034(se) (.088) (.090)

OLSy x 1_ˆ 1.1011 1.1924

(se) (.109) (.195)IV zy x

2_ˆ 1.3451 .1724

(se) (.256) (.797)IV zy x

3_ˆ .9640 1.7657

(se) (.095) (.172)IV zy x

10.3.3c An Illustration Using a Wage Equation


21 2 3 4ln WAGE EDUC EXPER EXPER e

2ln = .5220 .1075 .0416 .0008

(se) (.1986) (.0141) (.0132) (.0004)

WAGE EDUC EXPER EXPER

10.3.3c An Illustration Using a Wage Equation


2 9.7751 .0489 .0013 .2677 (se) (.4249) (.0417) (.0012) (.0311) EDUC EXPER EXPER MOTHEREDUC

2ln .1982 .0493 .0449 .0009

(se) (.4729) (.0374) (.0136) (.0004)


Check it out: as expected much lower than from OLS!!!

We hope for a high t ratio here

Principles of Econometrics, 3rd Edition 43

Use mroz.dta

summarizedrop if lfp==0gen lwage = log(wage)gen exper2 = exper^2

* Basic OLS estimationreg lwage educ exper exper2estimates store ls

* IV estimationreg educ exper exper2 mothereducpredict educ_hatreg lwage educ_hat exper exper2

But check that the latter will give you wrong s.e. so not reccommended to run 2SLS manually

Include in z all G exogenous variables and the instruments available

Exogenous variablesInstrument themselves!


In econometrics, two-stage least squares (TSLS or 2SLS) and instrumental variables (IV) estimation are often used interchangeably

The `two-stage' terminology comes from the time when the easiest way to estimate the model was to actually use two separate least squares regressions

With better software, the computation is done in a single step to ensure the other model statistics are computed correctly


In econometrics, two-stage least squares (TSLS or 2SLS) and instrumental variables (IV) estimation are often used interchangeablyThe `two-stage' terminology comes from the time when the easiest way to estimate the model was to actually use two separate least squares regressions

_cons -.5220406 .1986321 -2.63 0.009 -.9124667 -.1316144 exper2 -.0008112 .0003932 -2.06 0.040 -.0015841 -.0000382 exper .0415665 .0131752 3.15 0.002 .0156697 .0674633 educ .1074896 .0141465 7.60 0.000 .0796837 .1352956 lwage Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 223.327442 427 .523015086 Root MSE = .66642 Adj R-squared = 0.1509 Residual 188.305145 424 .444115908 R-squared = 0.1568 Model 35.0222967 3 11.6740989 Prob > F = 0.0000 F( 3, 424) = 26.29 Source SS df MS Number of obs = 428

. reg lwage educ exper exper2


_cons 9.775103 .4238886 23.06 0.000 8.941918 10.60829 mothereduc .2676908 .0311298 8.60 0.000 .2065029 .3288787 exper2 -.0012811 .0012449 -1.03 0.304 -.003728 .0011659 exper .0488615 .0416693 1.17 0.242 -.0330425 .1307655 educ Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 2230.19626 427 5.22294206 Root MSE = 2.1111 Adj R-squared = 0.1467 Residual 1889.65843 424 4.45674158 R-squared = 0.1527 Model 340.537834 3 113.512611 Prob > F = 0.0000 F( 3, 424) = 25.47 Source SS df MS Number of obs = 428

. reg educ exper exper2 mothereduc

First stage or “reduced form” equation:

Good news!!


Second stage equation:

_cons .1981861 .4933427 0.40 0.688 -.7715157 1.167888 exper2 -.0009221 .000424 -2.17 0.030 -.0017554 -.0000887 exper .0448558 .0141644 3.17 0.002 .0170147 .072697 educ_hat .0492629 .0390562 1.26 0.208 -.0275049 .1260308 lwage Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 223.327442 427 .523015086 Root MSE = .70902 Adj R-squared = 0.0388 Residual 213.146238 424 .502703391 R-squared = 0.0456 Model 10.181204 3 3.39373467 Prob > F = 0.0002 F( 3, 424) = 6.75 Source SS df MS Number of obs = 428

. reg lwage educ_hat exper exper2

Wrong standard errors!!!



Correct standard errors!!!

Instruments: exper exper2 mothereducInstrumented: educ _cons .1981861 .4706623 0.42 0.674 -.7242952 1.120667 exper2 -.0009221 .0004045 -2.28 0.023 -.0017148 -.0001293 exper .0448558 .0135132 3.32 0.001 .0183704 .0713413 educ .049263 .0372607 1.32 0.186 -.0237666 .1222925 lwage Coef. Std. Err. z P>|z| [95% Conf. Interval]

Root MSE = .67642 R-squared = 0.1231 Prob > chi2 = 0.0001 Wald chi2(3) = 22.25Instrumental variables (2SLS) regression Number of obs = 428

. ivregress 2sls lwage (educ=mothereduc) exper exper2



“Correcter” standard errors Wit a small sample and robust To heteroskedasticity!!!

Instruments: exper exper2 mothereducInstrumented: educ _cons .1981861 .4891462 0.41 0.686 -.7632673 1.159639 exper2 -.0009221 .0004319 -2.14 0.033 -.001771 -.0000732 exper .0448558 .0156038 2.87 0.004 .0141853 .0755264 educ .049263 .0380396 1.30 0.196 -.0255067 .1240326 lwage Coef. Std. Err. t P>|t| [95% Conf. Interval] Robust

Root MSE = .6796 Adj R-squared = 0.1169 R-squared = 0.1231 Prob > F = 0.0010 F( 3, 424) = 5.50Instrumental variables (2SLS) regression Number of obs = 428

. ivregress 2sls lwage (educ=mothereduc) exper exper2, vce(robust) small

10.3.4 Instrumental Variables Estimation With Surplus Instruments

A 2-step process. Regress x on a constant term, z and all other exogenous variables G,

and obtain the predicted values Use as an instrumental variable for x.


ˆ.x

x


Two-stage least squares (2SLS) estimator: Stage 1 is the regression of x on a constant term, z and all other

exogenous variables G, to obtain the predicted values . This first

stage is called the reduced form model estimation. Stage 2 is ordinary least squares estimation of the simple linear

regression


(10.23)

x

1 2 î i iy x error



(10.24)

(10.25)

2

2 2ˆvar

îx x

2

1 22ˆ ˆ

ˆ2

i i

IV

y x

N

2

2 2

ˆˆvarˆ

IV

ix x

10.3.4b An Illustration Using a Wage Equation


If we regress education on all the exogenous variables and the TWO instruments:

These look promising In fact: F test saysNull hypothesis: the regression parameters are zero for the variables mothereduc, fathereduc Test statistic: F(2, 423) = 55.4003, p-value 4.26891e-022

Rule of thumb for strong instruments: F>10

10.3.4b An Illustration Using a Wage Equation

ivregress 2sls lwage (educ=mothereduc fathereduc) exper exper2, small estimates store iv


2ln = .0481 .0614 .0442 .0009

(se) (.4003) (.0314) (.0134) (.0004)


With the additional instrument, we achieve a significant result in this case

10.3.5 Instrumental Variables Estimation in a General Model


Is a bit more complex, but the idea is that you need at least as many Instruments as you have endogenous variables

You cannot use the F test we just saw to test for the weakness of the instruments

10.3.5a Hypothesis Testing with Instrumental Variables Estimates

When testing the null hypothesis use of the test statistic

is valid in large samples. It is common, but not

universal, practice to use critical values, and p-values, based on the

Student-t distribution rather than the more strictly appropriate N(0,1)

distribution. The reason is that tests based on the t-distribution tend to

work better in samples of data that are not large.


0 : kH c

ˆ ˆsek kt c

10.3.5a Hypothesis Testing with Instrumental Variables Estimates

When testing a joint hypothesis, such as , the test

may be based on the chi-square distribution with the number of

degrees of freedom equal to the number of hypotheses (J) being

tested. The test itself may be called a “Wald” test, or a likelihood

ratio (LR) test, or a Lagrange multiplier (LM) test. These testing

procedures are all asymptotically equivalent

Another complication: you might need to use tests that are robust to

heteroskedasticity and/or autocorrelation…


0 2 2 3 3: ,H c c

10.3.5b Goodness of Fit with Instrumental Variables Estimates

Unfortunately R2 can be negative when based on IV estimates.

Therefore the use of measures like R2 outside the context of the least

squares estimation should be avoided (even if GRETL and STATA

produce one!)Slide 10-58Principles of Econometrics, 3rd Edition

1 2

1 2

22 2

ˆ ˆˆ

ˆ1 i i

y x e

e y x

R e y y

10.4 Specification Tests in IV

Can we test for whether x is correlated with the error term? This might

give us a guide of when to use least squares and when to use IV

estimators. TEST OF EXOGENEITY: (Durbin-Wu) HAUSMAN TEST

Can we test whether our instrument is sufficiently strong to avoid the

problems associated with “weak” instruments? TESTS FOR WEAK

INSTRUMENTS

Can we test if our instrument is valid, and uncorrelated with the regression

error, as required? SOMETIMES ONLY WITH A SARGAN TEST


10.4.1 The Hausman Test for Endogeneity

If the null hypothesis is true, both OLS and the IV estimator are

consistent. If the null hypothesis holds, use the more efficient

estimator, OLS.

If the null hypothesis is false, OLS is not consistent, and the IV

estimator is consistent, so use the IV estimator.


0 1: cov , 0 : cov , 0i i i iH x e H x e


Let z1 and z2 be instrumental variables for x.

1. Estimate the model by least squares, and obtain

the residuals . If there are more than one

explanatory variables that are being tested for endogeneity, repeat this

estimation for each one, using all available instrumental variables in each

regression.


1 2i i iy x e

1 1 1 2 2i i i ix z z v

1 1 1 2 2ˆ ˆî i i iv x z z


2. Include the residuals computed in step 1 as an explanatory variable

in the original regression, Estimate this

"artificial regression" by least squares, and employ the usual t-test

for the hypothesis of significance


1 2 ˆ .i i i iy x v e

0

1

: 0 no correlation between and : 0 correlation between and

i i

i i

H x eH x e


3. If more than one variable is being tested for endogeneity, the test will be

an F-test of joint significance of the coefficients on the included

residuals.

Note: This is a test for the exogeneity of the regressors xi and not for the exogeneity of the instruments zi. If the instruments are not valid, the Hausman test is not valid either.



* Hausman test regression based

reg educ exper exper2 mothereduc fathereduc

predict vhat, residuals

reg lwage educ exper exper2 vhat

reg lwage educ exper exper2 vhat, vce(robust)

There are several different ways of computing this test, so don't worry if your result from other software packages differs from the one you compute manually using the above script




so

_cons .0481003 .4221019 0.11 0.909 -.7815781 .8777787 vhat .0581666 .0364135 1.60 0.111 -.0134073 .1297405 exper2 -.000899 .0004152 -2.16 0.031 -.0017152 -.0000828 exper .0441704 .0151219 2.92 0.004 .0144469 .0738939 educ .0613966 .0326667 1.88 0.061 -.0028127 .125606 lwage Coef. Std. Err. t P>|t| [95% Conf. Interval] Robust

Root MSE = .66502 R-squared = 0.1624 Prob > F = 0.0000 F( 4, 423) = 21.52Linear regression Number of obs = 428

. reg lwage educ exper exper2 vhat, vce(robust)

.

So education is only barely exogenous at about 10%



so

So education is only barely exogenous at about 10%

hausman iv ls, constant sigmamore

(V_b-V_B is not positive definite) Prob>chi2 = 0.0954 = 2.78 chi2(1) = (b-B)'[(V_b-V_B)^(-1)](b-B)

Test: Ho: difference in coefficients not systematic

B = inconsistent under Ha, efficient under Ho; obtained from regress b = consistent under Ho and Ha; obtained from ivregress _cons .0481003 -.5220406 .5701409 .3418964 exper2 -.000899 -.0008112 -.0000878 .0000526 exper .0441704 .0415665 .0026039 .0015615 educ .0613966 .1074896 -.046093 .0276406 iv ls Difference S.E. (b) (B) (b-B) sqrt(diag(V_b-V_B)) Coefficients

Forces Stata to use the OLS Residuals when calculating the Error variance in both estimators

10.4.2 Testing for Weak Instruments

If we have L > 1 instruments available then the reduced form equation is


1 2 2 1 1G G G Gy x x x e

1 1 2 2 1 1G G Gx x x z v

1 1 2 2 1 1G G G L Lx x x z z v


Then test with an F test whether the instruments help to determine thevalue of the endogenous variable. Rule of thumb F > 10 for one

endogenous regressor


1 2 2 1 1G G G Gy x x x e

1 1 2 2 1 1G G Gx x x z v

1 1 2 2 1 1G G G L Lx x x z z v


As we saw before:

* Testing for weak instruments reg educ exper exper2 mothereduc reg educ exper exper2 fathereduc reg educ exper exper2 mothereduc fathereduc test mothereduc fathereduc



* Testing for weak instruments using estativregress 2sls lwage (educ=mothereduc fathereduc) exper exper2, smallestat firststage


so

LIML Size of nominal 5% Wald test 8.68 5.33 4.42 3.92 2SLS Size of nominal 5% Wald test 19.93 11.59 8.75 7.25 10% 15% 20% 25% 2SLS relative bias (not available) 5% 10% 20% 30% Ho: Instruments are weak # of excluded instruments: 2 Critical Values # of endogenous regressors: 1

Minimum eigenvalue statistic = 55.4003

educ 0.2115 0.2040 0.2076 55.4003 0.0000 Variable R-sq. R-sq. R-sq. F(2,423) Prob > F Adjusted Partial First-stage regression summary statistics

. estat firststage

.

We are quite alright here


* Robust testsreg educ exper exper2 mothereduc fathereduc, vce(robust)test mothereduc fathereduc


so

Prob > F = 0.0000 F( 2, 423) = 49.53

( 2) fathereduc = 0 ( 1) mothereduc = 0

. test mothereduc fathereduc

.

_cons 9.10264 .4241444 21.46 0.000 8.268947 9.936333 fathereduc .1895484 .0324419 5.84 0.000 .125781 .2533159 mothereduc .157597 .0354502 4.45 0.000 .0879165 .2272776 exper2 -.0010091 .0013233 -0.76 0.446 -.0036101 .0015919 exper .0452254 .0419107 1.08 0.281 -.0371538 .1276046 educ Coef. Std. Err. t P>|t| [95% Conf. Interval] Robust

Root MSE = 2.039 R-squared = 0.2115 Prob > F = 0.0000 F( 4, 423) = 25.76Linear regression Number of obs = 428

. reg educ exper exper2 mothereduc fathereduc, vce(robust)

.

Still alright


* Robust tests using ivregressivregress 2sls lwage (educ=mothereduc fathereduc) exper exper2, small vce(robust)estat firststage


so educ 0.2115 0.2040 0.2076 49.5266 0.0000 Variable R-sq. R-sq. R-sq. F(2,423) Prob > F Adjusted Partial Robust First-stage regression summary statistics

. estat firststage

.

Still alright

10.4.3 Testing Instrument Validity

We want to check that the instrument is itself exogenous…but you can only do it for

surplus instruments if you have them (overidentified equation)…

1. Compute the IV estimates using all available instruments, including the G

variables x1=1, x2, …, xG that are presumed to be exogenous, and the L

instruments z1, …, zL.

2. Obtain the residuals


ˆk

1 2 2ˆ ˆ ˆˆ .K Ke y x x


3. Regress on all the available instruments described in step 1.

4. Compute NR2 from this regression, where N is the sample size and

R2 is the usual goodness-of-fit measure.

5. If all of the surplus moment conditions are valid, then

If the value of the test statistic exceeds the 100(1−α)-percentile from

the distribution, then we conclude that at least one of the

surplus moment conditions restrictions is not valid.


e

2 2( )~ .L BNR

2( )L B



# Sargan test of overidentificationivregress 2sls lwage (educ=mothereduc fathereduc) exper exper2, smallpredict ehat, residualsquietly reg ehat mothereduc fathereduc exper exper2scalar nr2 = e(N)*e(r2)scalar chic = invchi2tail(1,.05)scalar pvalue = chi2tail(1,nr2)di "NR^2 test of overidentifying restriction = " nr2di "Chi-square critical value 1 df, .05 level = " chicdi "p value for overidentifying test 1 df, .05 level = " pvalue

You should learn about the Cragg-Uhler test if you end up having more than one endogenous variable



# Sargan test of overidentification


so

p value for overidentifying test 1 df, .05 level = .53863714. di "p value for overidentifying test 1 df, .05 level = " pvalue.

Chi-square critical value 1 df, .05 level = 3.8414588. di "Chi-square critical value 1 df, .05 level = " chic.

NR^2 test of overidentifying restriction = .37807151. di "NR^2 test of overidentifying restriction = " nr2.

So we are quite safe here…



# Sargan test of overidentification


so

So we are quite safe here…

Basmann chi2(1) = .373985 (p = 0.5408) Sargan (score) chi2(1) = .378071 (p = 0.5386)

Tests of overidentifying restrictions:

. estat overid

.

. quietly ivregress 2sls lwage (educ=mothereduc fathereduc) exper exper2, small

Keywords


asymptotic properties conditional expectation endogenous variables errors-in-variables exogenous variables finite sample properties Hausman test instrumental variable instrumental variable estimator just identified equations large sample properties over identified equations population moments random sampling reduced form equation

sample moments simultaneous equations bias test of surplus moment conditions two-stage least squares estimation weak instruments

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Chapter 10