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Endogenous Regressors and Instrumental Variables Estimation. Chapter 10. 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 - PowerPoint PPT Presentation
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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
Show Kennedy;s graphs and explain “regression”
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)
Slide 10-3Principles of Econometrics, 3rd Edition
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.
Slide 10-5Principles of Econometrics, 3rd Edition
1 2i i iy x e
, 1, ,i ix y i N
10.1 Linear Regression With Random X’s
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 .
Slide 10-6Principles of Econometrics, 3rd Edition
| 0.i iE e x
| 0i iE e x cov , 0i ix e
cov , 0i ix e | 0i iE e x
10.1 Linear Regression With Random X’s
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.
Slide 10-7Principles of Econometrics, 3rd Edition
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.
Slide 10-8Principles of Econometrics, 3rd Edition
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.
Slide 10-9Principles of Econometrics, 3rd Edition
10.1.2 Asymptotic Properties of the OLS Estimator: X Not Random
Figure 10.1 An illustration of consistency
Slide 10-10Principles of Econometrics, 3rd Edition
10.1.2 Asymptotic Properties of the OLS Estimator: X Not Random
Slide 10-11Principles of Econometrics, 3rd Edition
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*
Slide 10-12Principles of Econometrics, 3rd Edition
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
10.1.3 Asymptotic Properties of the OLS Estimator: X Random
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.
Slide 10-13Principles of Econometrics, 3rd Edition
10.1.3 Asymptotic Properties of the OLS Estimator: X Random
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.
Slide 10-14Principles of Econometrics, 3rd Edition
cov , 0i ix e
10.1.4 Why OLS Fails
Figure 10.2 Plot of correlated x and e
Slide 10-15Principles of Econometrics, 3rd Edition
10.1.4 Why OLS Fails
Slide 10-16Principles of Econometrics, 3rd Edition
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 …
10.1.4 Why OLS Fails
Figure 10.3 Plot of data, true and fitted regressions
Slide 10-17Principles of Econometrics, 3rd Edition
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.”
Slide 10-18Principles of Econometrics, 3rd Edition
10.2.1 Measurement Error
Slide 10-19Principles of Econometrics, 3rd Edition
(10.1)
(10.2)
*1 2i i iy x v
*i i ix x u
X Y
vu
eX Y
10.2.1 Measurement Error
Slide 10-20Principles of Econometrics, 3rd Edition
(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
10.2.1 Measurement Error
Slide 10-21Principles of Econometrics, 3rd Edition
(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
Slide 10-22Principles of Econometrics, 3rd Edition
(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!
.
Slide 10-23Principles of Econometrics, 3rd Edition
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.
Slide 10-24Principles of Econometrics, 3rd Edition
(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.
Slide 10-25Principles of Econometrics, 3rd Edition
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.
Slide 10-26Principles of Econometrics, 3rd Edition
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
10.3.3 Instrumental Variables Estimation in the Simple Linear Regression Model
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
Slide 10-28Principles of Econometrics, 3rd Edition
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
10.3.3 Instrumental Variables Estimation in the Simple Linear Regression Model
Slide 10-30Principles of Econometrics, 3rd Edition
(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…
10.3.3 Instrumental Variables Estimation in the Simple Linear Regression Model
Slide 10-31Principles of Econometrics, 3rd Edition
(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
10.3.3 Instrumental Variables Estimation in the Simple Linear Regression Model
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
Slide 10-32Principles of Econometrics, 3rd Edition
(10.19)
0.i iE z e
2
2 2 22ˆ ~ ,
zx i
Nr x x
10.3.3 Instrumental Variables Estimation in the Simple Linear Regression Model
The error variance is estimated using the estimator
Slide 10-33Principles of Econometrics, 3rd Edition
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: EDUC = α0 + α1Z + ω (‘reduced form equation’)
Structural model: WAGES = β0 + β1EDUC + ε
Also: 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
Slide 10-39Principles of Econometrics, 3rd Edition
(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
Slide 10-40Principles of Econometrics, 3rd Edition
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
Slide 10-41Principles of Econometrics, 3rd Edition
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
Slide 10-42Principles of Econometrics, 3rd Edition
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)
WAGE EDUC EXPER EXPER
Check it out: as expected much lower than from OLS!!!
We hope for a high t ratio here
Principles of Econometrics, 3rd Edition 43
open "@gretldir\data\poe\mroz.gdt"logs wagesquare exper
list x = const educ exper sq_experlist z = const exper sq_exper mothereduc# least squares and IV estimation of wage eqols l_wage xtsls l_wage x ; z
# tsls--manuallysmpl wage>0 --restrictols educ zseries educ_hat = $yhatols l_wage const educ_hat exper sq_exper
But check that the latter will give you wrong s.e. so not reccommended
Include in z all G exogenous variables and the instruments available
Exogenous variablesInstrument themselves!
Principles of Econometrics, 3rd Edition 44
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
Unfortunately GRETL outputs R2 for the IV regression; you should ignore it! It is based on the correlation of y and yhat, so it does not have the usual interpretation…
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.
Slide 10-45Principles of Econometrics, 3rd Edition
ˆ.x
x
10.3.4 Instrumental Variables Estimation With Surplus Instruments
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
Slide 10-46Principles of Econometrics, 3rd Edition
(10.23)
x
1 2 ˆi i iy x error
10.3.4 Instrumental Variables Estimation With Surplus Instruments
Slide 10-47Principles of Econometrics, 3rd Edition
(10.24)
(10.25)
2
2 2ˆvar
ˆix 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
Slide 10-48Principles of Econometrics, 3rd Edition
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
Slide 10-49Principles of Econometrics, 3rd Edition
2ln = .0481 .0614 .0442 .0009 (se) (.4003) (.0314) (.0134) (.0004)
WAGE EDUC EXPER EXPER
With the additional instrument, we achieve a significant result in this case
10.3.5 Instrumental Variables Estimation in a General Model
Slide 10-50Principles of Econometrics, 3rd Edition
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, but see Appendix for a test based on the notion of partial correlations
For example, run this experiment with the mroz.gdt data:# partial correlations--the FWL resultols educ const exper sq_experseries e1 = $uhatols mothereduc const exper sq_experseries e2 = $uhatols e1 e2corr e1 e2
10.3.5 Instrumental Variables Estimation in a General Model
Slide 10-51Principles of Econometrics, 3rd Edition
it is the independent correlation between the instrument and the endogenous regressor that is important. We can partial out the correlation in X that is due to the exogenous regressors. Whatever common variation that remains will measure the independent correlation between X and the instrument Z: we ask ourselves how much of the independent variation of educ is captured by the independent variation in mothereduc?
Partial correlations
For example, run this experiment with the mroz.gdt data:# partial correlations--the FWL resultols educ const exper sq_experseries e1 = $uhatols mothereduc const exper sq_experseries e2 = $uhatols e1 e2corr e1 e2
And compare your results with ols educ const mothereduc exper sq_exper
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.
Slide 10-52Principles of Econometrics, 3rd Edition
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…
Slide 10-53Principles of Econometrics, 3rd Edition
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 produces one!)
Slide 10-54Principles 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
Slide 10-55Principles of Econometrics, 3rd Edition
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.
Slide 10-56Principles of Econometrics, 3rd Edition
0 1: cov , 0 : cov , 0i i i iH x e H x e
10.4.1 The Hausman Test for Endogeneity
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.
Slide 10-57Principles of Econometrics, 3rd Edition
1 2i i iy x e
1 1 1 2 2i i i ix z z v
1 1 1 2 2ˆ ˆˆi i i iv x z z
10.4.1 The Hausman Test for Endogeneity
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
Slide 10-58Principles of Econometrics, 3rd Edition
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
10.4.1 The Hausman Test for Endogeneity
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.
Slide 10-59Principles of Econometrics, 3rd Edition
10.4.1 The Hausman Test for Endogeneity
# GRETL Hausman test (manually)
ols educ z --quiet
series ehat = $uhat
ols l_wage x ehat
Note: whenever you use 2SLS GRETLautomatically produces the test statistic for the Hausman test.
There are several different ways of computing it, so don't worry if it differs from the one you compute manually using the above script
Slide 10-60Principles of Econometrics, 3rd Edition
10.4.2 Testing for Weak Instruments
If we have L > 1 instruments available then the reduced form equation is
Slide 10-61Principles of Econometrics, 3rd Edition
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
10.4.2 Testing for Weak Instruments
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
Slide 10-62Principles of Econometrics, 3rd Edition
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
10.4.2 Testing for Weak Instruments
As we saw before:
open "@gretldir\data\poe\mroz.gdt"smpl wage>0 --restrictlogs wagesquare experlist x = const educ exper sq_experlist z2 = const exper sq_exper mothereduc fathereducols educ z2omit mothereduc fathereduc
Slide 10-63Principles of Econometrics, 3rd Edition
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
Slide 10-64Principles of Econometrics, 3rd Edition
ˆk
1 2 2ˆ ˆ ˆˆ .K Ke y x x
10.4.3 Testing Instrument Validity
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.
Slide 10-65Principles of Econometrics, 3rd Edition
e
2 2( )~ .L BNR
2( )L B
10.4.3 Testing Instrument Validity
Slide 10-66Principles of Econometrics, 3rd Edition
# Sargan test of overidentificationtsls l_wage x; z2series uhat2 = $uhatols uhat2 z2scalar test = $trsqpvalue X 1 test
tsls l_wage x ; z2
You should learn about the Cragg-Uhler test if you end up having more than one endogenous variable
Keywords
Slide 10-67Principles of Econometrics, 3rd Edition
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