1/69: topic 1.1 - descriptive statistics and linear regression microeconometric modeling william...
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1/69: Topic 1.1 - Descriptive Statistics and Linear Regression
Microeconometric Modeling
William GreeneStern School of BusinessNew York UniversityNew York NY USA
1.1 Descriptive Statistics and Linear Regression
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2/69: Topic 1.1 - Descriptive Statistics and Linear Regression
Data Description
• Basic Statistics• Tables• Histogram• Box Plot• Histogram• Kernel Density Estimator
Linear Regression Model
• Linear Model• Specification & Estimation
• Nonlinearities• Interactions
• Inference - Testing• Wald• F• LM
• Prediction and Model Fit• Endogeneity
• 2SLS• Control Function• Hausman Test
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3/69: Topic 1.1 - Descriptive Statistics and Linear Regression
Cornwell and Rupert Panel DataCornwell and Rupert Returns to Schooling Data, 595 Individuals, 7 YearsVariables in the file are
EXP = work experienceWKS = weeks workedOCC = occupation, 1 if blue collar, IND = 1 if manufacturing industrySOUTH = 1 if resides in southSMSA = 1 if resides in a city (SMSA)MS = 1 if marriedFEM = 1 if femaleUNION = 1 if wage set by union contractED = years of educationLWAGE = log of wage = dependent variable in regressions
These data were analyzed in Cornwell, C. and Rupert, P., "Efficient Estimation with Panel Data: An Empirical Comparison of Instrumental Variable Estimators," Journal of Applied Econometrics, 3, 1988, pp. 149-155.
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5/69: Topic 1.1 - Descriptive Statistics and Linear Regression
Objective: Impact of Education on (log) Wage
Specification: What is the right model to use to analyze this association?
Estimation Inference Analysis
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6/69: Topic 1.1 - Descriptive Statistics and Linear Regression
Simple Linear Regression
LWAGE = 5.8388 + 0.0652*ED
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7/69: Topic 1.1 - Descriptive Statistics and Linear Regression
Multiple Regression
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8/69: Topic 1.1 - Descriptive Statistics and Linear Regression
Nonlinear Specification: Quadratic Effect of Experience
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9/69: Topic 1.1 - Descriptive Statistics and Linear Regression
Partial EffectsCoefficients do not tell the story
Education: .05654Experience .04045 - 2*.00068*ExpFEM -.38922
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10/69: Topic 1.1 - Descriptive Statistics and Linear Regression
Effect of Experience = .04045 - 2*.00068*ExpPositive from 1 to 30, negative after.
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11/69: Topic 1.1 - Descriptive Statistics and Linear Regression
Model Implication: Effect of Experience and Male vs. Female
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12/69: Topic 1.1 - Descriptive Statistics and Linear Regression
Interaction EffectGender Difference in Partial
Effects
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13/69: Topic 1.1 - Descriptive Statistics and Linear Regression
Partial Effect of a Year of EducationE[logWage]/ED=ED + ED*FEM *FEMNote, the effect is positive. Effect is larger for women.
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14/69: Topic 1.1 - Descriptive Statistics and Linear Regression
Gender Effect Varies by Years of Education
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15/69: Topic 1.1 - Descriptive Statistics and Linear Regression
Endogeneity
y = X+ε, Definition: E[ε|x]≠0 Why not? The most common reasons:
Omitted variables Unobserved heterogeneity (equivalent to omitted variables) Measurement error on the RHS (equivalent to omitted
variables) Endogenous sampling and attrition
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16/69: Topic 1.1 - Descriptive Statistics and Linear Regression
The Effect of Education on LWAGE
1 2 3 4 ... ε
What is ε? ,... + everything else
= f( , , , ,
Ability, Motivation
Ability, Motiva .ti .on . )
EDUC
ED
LWAGE EXP
GENDER SMSA SOUTHUC
2EXP
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17/69: Topic 1.1 - Descriptive Statistics and Linear Regression
What Influences LWAGE?
1 2
3 4
Ability, Motivation
Ability, Motivat
( , ,...)
...
ε( )
Increased is associated with increases in
ion
Ability
Ability, Motivatio, ,n(
EDUC
E
LWAGE X
EXP
DUC X
2EXP
2
...) and ε( )
What looks like an effect due to increase in may
be an increase in . The estimate of picks up
the effect of and the hidden effect of .
Ability, Motivation
Ability
Ability
EDUC
EDUC
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18/69: Topic 1.1 - Descriptive Statistics and Linear Regression
An Exogenous Influence
1 2
3 4
( , , ,...)
...
ε( )
Increased is asso
Abili
ciate
ty, Motivation
Ability, Motivation
Ability, Motivation
d with increases in
( , , ,..
EDU ZC
EDUC
LWAGE X
EXP
Z
ZX
2EXP
2
.) and not ε( )
An effect due to the effect of an increase on will
only be an increase in . The estimate of picks up
the effect of only.
Ability, Motiv
ation
EDUC
EDUC
ED
Z
Z
UC
is an Instrumental Variable
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19/69: Topic 1.1 - Descriptive Statistics and Linear Regression
Instrumental Variables Structure
LWAGE (ED,EXP,EXPSQ,WKS,OCC, SOUTH,SMSA,UNION)
ED (MS, FEM)
Reduced Form: LWAGE[ ED (MS, FEM), EXP,EXPSQ,WKS,OCC, SOUTH,SMSA,UNION ]
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20/69: Topic 1.1 - Descriptive Statistics and Linear Regression
Two Stage Least Squares Strategy
Reduced Form: LWAGE[ ED (MS, FEM,X), EXP,EXPSQ,WKS,OCC, SOUTH,SMSA,UNION ]
Strategy (1) Purge ED of the influence of everything but
MS, FEM (and the other variables). Predict ED using all exogenous information in the sample (X and Z).
(2) Regress LWAGE on this prediction of ED and everything else.
Standard errors must be adjusted for the predicted ED
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21/69: Topic 1.1 - Descriptive Statistics and Linear Regression
OLS
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22/69: Topic 1.1 - Descriptive Statistics and Linear Regression
The extreme result for the coefficient on ED is probably due to the fact that the instruments, MS and FEM are dummy variables. There is not enough variation in these variables.
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23/69: Topic 1.1 - Descriptive Statistics and Linear Regression
Source of Endogeneity
LWAGE = f(ED, EXP,EXPSQ,WKS,OCC, SOUTH,SMSA,UNION) +
ED = f(MS,FEM, EXP,EXPSQ,WKS,OCC, SOUTH,SMSA,UNION) + u
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24/69: Topic 1.1 - Descriptive Statistics and Linear Regression
Remove the Endogeneity by Usinga Control Function
LWAGE = f(ED, EXP,EXPSQ,WKS,OCC, SOUTH,SMSA,UNION) + u +
LWAGE = f(ED, EXP,EXPSQ,WKS,OCC, SOUTH,SMSA,UNION) + u +
Strategy Estimate u Add u to the equation. ED is uncorrelated with
when u is in the equation.
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25/69: Topic 1.1 - Descriptive Statistics and Linear Regression
Auxiliary Regression for ED to Obtain Residuals
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26/69: Topic 1.1 - Descriptive Statistics and Linear Regression
OLS with Residual (Control Function) Added
2SLS
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27/69: Topic 1.1 - Descriptive Statistics and Linear Regression
A Warning About Control Functions
Sum of squares is not computed correctly because U is in the regression.A general result. Control function estimators usually require a fix to the estimated covariance matrix for the estimator.
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28/69: Topic 1.1 - Descriptive Statistics and Linear Regression
An Endogeneity Test? (Hausman)
Exogenous Endogenous
OLS Consistent, Efficient Inconsistent 2SLS Consistent, Inefficient Consistent
Base a test on d = b2SLS - bOLS
Use a Wald statistic, d’[Var(d)]-1d
What to use for the variance matrix? Hausman: V2SLS - VOLS
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29/69: Topic 1.1 - Descriptive Statistics and Linear Regression
Hausman Test
Chi squared with 1 degree of freedom
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30/69: Topic 1.1 - Descriptive Statistics and Linear Regression
Hausman Test: One at a Time?
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Endogeneity Test: Wu
Considerable complication in Hausman test (Greene (2012), pp. 234-237)
Simplification: Wu test. Regress y on X and estimated for the
endogenous part of X. Then use an ordinary Wald test.
Variable addition test
X̂
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32/69: Topic 1.1 - Descriptive Statistics and Linear Regression
Wu Test