Transcript Part 2: Basic Econometrics [ 1/51] Econometric Analysis of Panel Data William Greene Department of Economics Stern School of Business.

Part 2: Basic Econometrics [ 1/51]
Econometric Analysis of Panel Data
William Greene
Department of Economics
Stern School of Business
Part 2: Basic Econometrics [ 2/51]
Part 2: Basic Econometrics [ 3/51]
Part 2: Basic Econometrics [ 4/51]
www.oft.gov.uk/shared_oft/reports/Evaluating-OFTs-work/oft1416.pdf
Part 2: Basic Econometrics [ 5/51]
Econometrics

Theoretical foundations


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Microeconometrics and macroeconometrics
Behavioral modeling
Statistical foundations: Econometric methods
Mathematical elements: the usual
‘Model’ building – the econometric model
Part 2: Basic Econometrics [ 6/51]
Estimation Platforms

Model based
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Kernels and smoothing methods (nonparametric)
Semiparametric analysis
Parametric analysis
Moments and quantiles (semiparametric)
Likelihood and M- estimators (parametric)
Methodology based (?)
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
Classical – parametric and semiparametric
Bayesian – strongly parametric
Part 2: Basic Econometrics [ 7/51]
Trends in Econometrics

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Small structural models vs. large scale multiple equation
models
Non- and semiparametric methods vs. parametric
Robust methods – GMM (paradigm shift? Nobel prize)
Unit roots, cointegration and macroeconometrics
Nonlinear modeling and the role of software
Behavioral and structural modeling vs. “reduced form,”
“covariance analysis”
Pervasiveness of an econometrics paradigm
Identification and “causal” effects
Part 2: Basic Econometrics [ 8/51]
Objectives in Model Building

Specification: guided by underlying theory



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Estimation: coefficients, partial effects, model
implications
Statistical inference: hypothesis testing
Prediction: individual and aggregate
Model assessment (fit, adequacy) and evaluation
Model extensions



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Modeling framework
Functional forms
Interdependencies, multiple part models
Heterogeneity
Endogeneity
Exploration: Estimation and inference methods
Part 2: Basic Econometrics [ 9/51]
Regression Basics
The “MODEL”

Modeling the conditional mean –
Regression
Other features of interest




Modeling quantiles
Conditional variances or covariances
Modeling probabilities for discrete choice
Modeling other features of the population
Part 2: Basic Econometrics [ 10/51]
Application: Health Care Usage
German Health Care Usage Data, 7,293 Individuals, Varying Numbers of Periods
Data downloaded from Journal of Applied Econometrics Archive. They can be used for regression, count models,
binary choice, ordered choice, and bivariate binary choice. There are altogether 27,326 observations. The number
of observations ranges from 1 to 7. (Frequencies are: 1=1525, 2=2158, 3=825, 4=926, 5=1051, 6=1000, 7=987).
(Downloaded from the JAE Archive)
Variables in the file are
DOCTOR = 1(Number of doctor visits > 0)
HOSPITAL = 1(Number of hospital visits > 0)
HSAT
= health satisfaction, coded 0 (low) - 10 (high)
DOCVIS
= number of doctor visits in last three months
HOSPVIS = number of hospital visits in last calendar year
PUBLIC
= insured in public health insurance = 1; otherwise = 0
ADDON
= insured by add-on insurance = 1; otherswise = 0
HHNINC = household nominal monthly net income in German marks / 10000.
(4 observations with income=0 were dropped)
HHKIDS
= children under age 16 in the household = 1; otherwise = 0
EDUC
= years of schooling
AGE
= age in years
MARRIED = marital status
Part 2: Basic Econometrics [ 11/51]
Household Income
Kernel Density Estimator
Histogram
Part 2: Basic Econometrics [ 12/51]
Regression – Income on
Education
---------------------------------------------------------------------Ordinary
least squares regression ............
LHS=LOGINC
Mean
=
-.92882
Standard deviation
=
.47948
Number of observs.
=
887
Model size
Parameters
=
2
Degrees of freedom
=
885
Residuals
Sum of squares
=
183.19359
Standard error of e =
.45497
Fit
R-squared
=
.10064
Adjusted R-squared
=
.09962
Model test
F[ 1,
885] (prob) =
99.0(.0000)
Diagnostic
Log likelihood
=
-559.06527
Restricted(b=0)
=
-606.10609
Chi-sq [ 1] (prob) =
94.1(.0000)
Info criter. LogAmemiya Prd. Crt. =
-1.57279
--------+------------------------------------------------------------Variable| Coefficient
Standard Error b/St.Er. P[|Z|>z]
Mean of X
--------+------------------------------------------------------------Constant|
-1.71604***
.08057
-21.299
.0000
EDUC|
.07176***
.00721
9.951
.0000
10.9707
--------+------------------------------------------------------------Note: ***, **, * = Significance at 1%, 5%, 10% level.
----------------------------------------------------------------------
Part 2: Basic Econometrics [ 13/51]
Specification and Functional Form
---------------------------------------------------------------------Ordinary
least squares regression ............
LHS=LOGINC
Mean
=
-.92882
Standard deviation
=
.47948
Number of observs.
=
887
Model size
Parameters
=
3
Degrees of freedom
=
884
Residuals
Sum of squares
=
183.00347
Standard error of e =
.45499
Fit
R-squared
=
.10157
Adjusted R-squared
=
.09954
Model test
F[ 2,
884] (prob) =
50.0(.0000)
Diagnostic
Log likelihood
=
-558.60477
Restricted(b=0)
=
-606.10609
Chi-sq [ 2] (prob) =
95.0(.0000)
Info criter. LogAmemiya Prd. Crt. =
-1.57158
--------+------------------------------------------------------------Variable| Coefficient
Standard Error b/St.Er. P[|Z|>z]
Mean of X
--------+------------------------------------------------------------Constant|
-1.68303***
.08763
-19.207
.0000
EDUC|
.06993***
.00746
9.375
.0000
10.9707
FEMALE|
-.03065
.03199
-.958
.3379
.42277
--------+-------------------------------------------------------------
Part 2: Basic Econometrics [ 14/51]
Interesting Partial Effects
---------------------------------------------------------------------Ordinary
least squares regression ............
LHS=LOGINC
Mean
=
-.92882
Standard deviation
=
.47948
Number of observs.
=
887
Model size
Parameters
=
5
Degrees of freedom
=
882
Residuals
Sum of squares
=
171.87964
Standard error of e =
.44145
Fit
R-squared
=
.15618
E[ Income | x]
  Age
Adjusted R-squared
=
.15235

Age
Model test
F[ 4,
882] (prob) =
40.8(.0000)
Diagnostic
Log likelihood
=
-530.79258
Restricted(b=0)
=
-606.10609
Chi-sq [ 4] (prob) =
150.6(.0000)
Info criter. LogAmemiya Prd. Crt. =
-1.62978
--------+------------------------------------------------------------Variable| Coefficient
Standard Error b/St.Er. P[|Z|>z]
Mean of X
--------+------------------------------------------------------------Constant|
-5.26676***
.56499
-9.322
.0000
EDUC|
.06469***
.00730
8.860
.0000
10.9707
FEMALE|
-.03683
.03134
-1.175
.2399
.42277
AGE|
.15567***
.02297
6.777
.0000
50.4780
AGE2|
-.00161***
.00023
-7.014
.0000
2620.79
--------+-------------------------------------------------------------
 2 Age  Age2
Part 2: Basic Econometrics [ 15/51]
Function: Log Income | Age
Partial Effect wrt Age
Part 2: Basic Econometrics [ 16/51]
A Statistical Relationship


A relationship of interest:
 Number of hospital visits: H = 0,1,2,…
 Covariates: x1=Age, x2=Sex, x3=Income, x4=Health
Causality and covariation
 Theoretical implications of ‘causation’
 Comovement and association
 Intervention of omitted or ‘latent’ variables
 Temporal relationship – movement of the “causal
variable” precedes the effect.
Part 2: Basic Econometrics [ 17/51]
(Endogeneity)
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
A relationship of interest:
 Number of hospital visits: H = 0,1,2,…
 Covariates: x1=Age, x2=Sex, x3=Income, x4=Health
Should Health be ‘Endogenous’ in this model?
 What do we mean by ‘Endogenous’
 What is an appropriate econometric method of
accommodating endogeneity?
Part 2: Basic Econometrics [ 18/51]
Models
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Conditional mean function: E[y | x]
Other conditional characteristics – what is ‘the model?’
 Conditional variance function: Var[y | x]
 Conditional quantiles, e.g., median [y | x]
 Other conditional moments
Conditional probabilities: P(y|x)
What is the sense in which “y varies with x?”
Part 2: Basic Econometrics [ 19/51]
Using the Model
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Understanding the relationship:
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Estimation of quantities of interest such as elasticities
Prediction of the outcome of interest
Control of the path of the outcome of interest
Part 2: Basic Econometrics [ 20/51]
Application: Doctor Visits
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German individual health care data: N=27,236
Model for number of visits to the doctor:
Poisson regression (fit by maximum likelihood)
E[V|Income]=exp(1.412 - .0745  income)
OLS Linear regression: g*(Income)=3.917 - .208  income
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H i s t o g ra m
11152
fo r N u m b e r o f D o c to r V i s i ts
8364
Frequency
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5576
2788
0
0
4
8
12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 88 92 96 100104108112116120
DO CVI S
Part 2: Basic Econometrics [ 21/51]
Conditional Mean
and Linear Projection
P ro j e c t i o n a n d Co n d i t i o n a l M e a n F u n c t i o n s
4. 31
3. 40
This area is
outside the range
of the data
Functi on
2. 48
Most of the
data are in
here
1. 57
. 66
- . 26
0
5
10
15
20
I NCO M E
CO NDM EAN
PRO JECTN
Notice the problem with the linear projection. Negative predictions.
Part 2: Basic Econometrics [ 22/51]
What About the Linear Projection?
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What we do when we linearly regress a variable
on a set of variables
Assuming there exists a conditional mean
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There usually exists a linear projection. Requires
finite variance of y.
Approximation to the conditional mean
If the conditional mean is linear
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Linear projection equals the conditional mean
Part 2: Basic Econometrics [ 23/51]
Partial Effects
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What did the model tell us?
Covariation and partial effects: How does the y
“vary” with the x?
Marginal Effects: Effect on what?????
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For continuous variables
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δ(x)=E[y|x]/x, usually not coefficients
For dummy variables
(x,d)=E[y|x,d=1] - E[y|x,d=0]
Elasticities: ε(x)=δ(x)  x / E[y|x]
Part 2: Basic Econometrics [ 24/51]
Average Partial Effects
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When δ(x) ≠β, APE = Ex[δ(x)]= x (x)f(x)dx
Approximation: Is δ(E[x]) = Ex[δ(x)]? (no)
Empirically: Estimated
APE = (1 /N)Ni=1ˆ
(xi )
Empirical approximation: Est.APE = ˆ
(x)
For the doctor visits model
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δ(x)= β exp(α+βx)=-.0745exp(1.412-.0745income)
Sample APE
= -.2373
Approximation
= -.2354
Slope of the linear projection = -.2083 (!)
Part 2: Basic Econometrics [ 25/51]
APE and PE at the Mean
δ(x)=E[y|x]/x, =E[x]
δ(x)  δ()+δ()(x-)+(1/2)δ()(x-)2 + 
E[δ(x)]=APE  δ() + (1/2)δ()2x
Implication: Computing the APE by averaging over observations (and
counting on the LLN and the Slutsky theorem) vs. computing partial
effects at the means of the data.
In the earlier example: Sample APE = -.2373
Approximation = -.2354
Part 2: Basic Econometrics [ 26/51]
The Linear Regression Model

y = X+ε, N observations, K columns in X,
including a column of ones.
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
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Standard assumptions about X
Standard assumptions about ε|X
E[ε|X]=0, E[ε]=0 and Cov[ε,x]=0
Regression?
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If E[y|X] = X then X is the projection of y on X
Part 2: Basic Econometrics [ 27/51]
Estimation of the Parameters
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Least squares, LAD, other estimators – we will
focus on least squares
b = (X'X )-1 X'y
2
s  e'e /N or e'e /(N-K)
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
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Classical vs. Bayesian estimation of 
Properties
Statistical inference: Hypothesis tests
Prediction (not this course)
Part 2: Basic Econometrics [ 28/51]
Properties of Least Squares


Finite sample properties: Unbiased, etc. No
longer interested in these.
Asymptotic properties
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Consistent? Under what assumptions?
Efficient?
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Contemporary work: Often not important
Efficiency within a class: GMM
Asymptotically normal: How is this established?
Robust estimation: To be considered later
Part 2: Basic Econometrics [ 29/51]
Least Squares Summary
ˆ  b, plim b = 

d
N(b  ) 
N{0, 2 [plim( X'X / N)]1 }
a
b 
N{, (2 / N)[plim( X'X / N)]1 }
Estimated Asy.Var[b]=s 2 ( X'X ) 1
Part 2: Basic Econometrics [ 30/51]
Hypothesis Testing

Nested vs. nonnested tests

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
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

y=b1x+e vs. y=b1x+b2z+e: Nested
y=bx+e vs. y=cz+u: Not nested
y=bx+e vs. logy=clogx: Not nested
y=bx+e; e ~ Normal vs. e ~ t[.]: Not nested
Fixed vs. random effects: Not nested
Logit vs. probit: Not nested
x is endogenous: Maybe nested. We’ll see …
Parametric restrictions



Linear: R-q = 0, R is JxK, J < K, full row rank
General: r(,q) = 0, r = a vector of J functions,
R (,q) = r(,q)/’.
Use r(,q)=0 for linear and nonlinear cases
Part 2: Basic Econometrics [ 31/51]
Example: Panel Data on Spanish Dairy Farms
N = 247 farms, T = 6 years (1993-1998)
Units
Mean
Output
Milk
Milk production (liters)
Input
Cows
# of milking cows
22.12
Input
Labor
# man-equivalent units
Input
Land
Input
Feed
131,107
Std. Dev.
Minimum
92,584
14,410
Maximum
727,281
11.27
4.5
82.3
1.67
0.55
1.0
4.0
Hectares of land devoted
to pasture and crops.
12.99
6.17
2.0
45.1
Total
amount
of
feedstuffs fed to dairy
cows (Kg)
57,941
47,981
3,924.14
376,732
Part 2: Basic Econometrics [ 32/51]
Application





y = log output
x = Cobb douglas production: x = 1,x1,x2,x3,x4
= constant and logs of 4 inputs (5 terms)
z = Translog terms, x12, x22, etc. and all cross products,
x1x2, x1x3, x1x4, x2x3, etc. (10 terms)
w = (x,z) (all 15 terms)
Null hypothesis is Cobb Douglas, alternative is translog
= Cobb-Douglas plus second order terms.
Part 2: Basic Econometrics [ 33/51]
Translog Regression Model
x
H0:z=0
Part 2: Basic Econometrics [ 34/51]
Wald Tests


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r(b,q)= close to zero?
Wald distance function:
r(b,q)’{Var[r(b,q)]}-1 r(b,q) 2[J]
Use the delta method to estimate Var[r(b,q)]



Est.Asy.Var[b]=s2(X’X)-1
Est.Asy.Var[r(b,q)]= R(b,q){s2(X’X)-1}R’(b,q)
The standard F test is a Wald test; JF = 2[J].
Part 2: Basic Econometrics [ 35/51]
Wald= b z - 0  {Var[b z - 0]}1 b z - 0 
Close
to 0?
Part 2: Basic Econometrics [ 36/51]
Likelihood Ratio Test



The normality assumption
Does it work ‘approximately?’
For any regression model yi = h(xi,)+εi where
εi ~N[0,2], (linear or nonlinear), at the linear (or
nonlinear) least squares estimator, however, computed,
with or without restrictions,
2
2
ˆ, 

logL(



/N)


(N/2)[1+log2

+log

ˆˆ
ˆ
ˆ ]
This forms the basis for likelihood ratio tests.
ˆunrestricted )  log L (
ˆrestricted )]
2[log L (
2

ˆ restricted
d
 Nlog 2

2 [ J ]

ˆunrestricted
Part 2: Basic Econometrics [ 37/51]
Part 2: Basic Econometrics [ 38/51]
Score or LM Test: General


Maximum Likelihood (ML) Estimation
A hypothesis test




H0: Restrictions on parameters are
true
H1: Restrictions on parameters are not true
Basis for the test: b0 = parameter estimate under H0
(i.e., restricted), b1 = unrestricted
Derivative results: For the likelihood function under H1,


logL1/ | =b1 = 0 (exactly, by definition)
logL1/ | =b0 ≠ 0. Is it close? If so, the restrictions look
reasonable
Part 2: Basic Econometrics [ 39/51]
Why is it the Lagrange Multiplier Test?
Maximize logL( ) subject to restrictions r ()= 0
 X 
such as Rβ - q = 0 or  Z = 0 when  =  , R = (0:I) and q = 0.
 Z 
Use LM approach:
Maximize wrt (  ) L* = logL( )   r ().


L * / ˆ R  logL(ˆ R ) / ˆ R  r(ˆ R ) / ˆ R   0 
 
FOC: 

 0
 L * /   
r (ˆ R )

If  = 0, the constraints are not binding and logL(ˆ R ) / ˆ R  0 so ˆ R  ˆ U .
If   0, the constraints are binding and logL(ˆ ) / ˆ  0.
R
Direct test: Test H 0 : = 0
Equivalent test: Test H 0LM : logL(ˆ R ) / ˆ R  0.
R
Part 2: Basic Econometrics [ 40/51]
Computing the LM Statistic
ˆ (1|0),i
g
N
2

e
1  x i 
 2   ei|0 , ei|0 =y i  x ib, s 02  i=1 i|0
N
s 0  zi 
1  x i 
1  X'e0  1  0 



  ei|0 = 2 
s 20  zi 
s 0  Z'e0  s 20  Z'e0 
1 2  xi   xi 
N
N
ˆ
ˆ (1|0),ig
ˆ(1|0),i  i=1 4 ei|0     '
H(1|0)  i=1g
s0
 zi   zi 
Note this is the middle matrix in the White estimator.
ˆ (1|0) =Ni=1g
ˆ (1|0),i = Ni=1
g
x 
and s 04 disappear from the product. Let w i0  ei|0  i  .
 zi 
Then LM=i'W0 (W0 W0 ) 1 W0 i, which is simple to compute.
 
The s 20
2
The derivation on page 64 of Wooldridge’s text is needlessly complex, and the second form of LM
is actually incorrect because the first derivatives are not ‘heteroscedasticity robust.’
Part 2: Basic Econometrics [ 41/51]
Application of the Score Test

Linear Model: Y = X+Zδ+ε


Test H0: δ=0
Restricted estimator is [b’,0’]’
Namelist ; X = a list… ; Z = a list … ; W = X,Z $
Regress ; Lhs = y ; Rhs = X ; Res = e $
Matrix ; list ; LM = e’ W * <W’[e^2]W> * W’ e $
Part 2: Basic Econometrics [ 42/51]
Restricted regression and
derivatives for the LM Test
Part 2: Basic Econometrics [ 43/51]
Tests for Omitted Variables
? Cobb - Douglas Model
Namelist ; X = One,x1,x2,x3,x4 $
? Translog second order terms, squares and cross products of logs
Namelist ; Z = x11,x22,x33,x44,x12,x13,x14,x23,x24,x34 $
? Restricted regression. Short. Has only the log terms
Regress ; Lhs = yit ; Rhs = X ; Res = e $
Calc
; LoglR = LogL ; RsqR = Rsqrd $
? LM statistic using basic matrix algebra
Namelist ; W = X,Z $
Matrix ; List ; LM = e'W * <W’[e^2]W> * W'e $
? LR statistic uses the full, long regression with all quadratic terms
Regress ; Lhs = yit ; Rhs = W $
Calc
; LoglU = LogL ; RsqU = Rsqrd ; List ; LR = 2*(Logl - LoglR) $
? Wald Statistic is just J*F for the translog terms
Calc
; List ; JF=col(Z)*((RsqU-RsqR)/col(Z)/((1-RsqU)/(n-kreg)) )$
Part 2: Basic Econometrics [ 44/51]
Regression Specifications
Part 2: Basic Econometrics [ 45/51]
Model Selection



Regression models: Fit measure = R2
Nested models: log likelihood, GMM criterion
function (distance function)
Nonnested models, nonlinear models:

Classical



Akaike information criterion= – (logL – 2K)/N
Bayes (Schwartz) information criterion = –(logL-K(logN))/N
Bayesian: Bayes factor = Posterior odds/Prior odds
(For noninformative priors, BF=ratio of posteriors)
Part 2: Basic Econometrics [ 46/51]
Remaining to Consider for the
Linear Regression Model

Failures of standard assumptions





Heteroscedasticity
Autocorrelation and Spatial Correlation
Robust estimation
Omitted variables
Measurement error
Part 2: Basic Econometrics [ 47/51]
Appendix:
Computing the
LM Statistic
Part 2: Basic Econometrics [ 48/51]
LM Test
ˆ
ˆ1|=b0  Ni=1g
ˆ (1|=b0 ),i  G'i
g
ˆ is a vector of derivatives.
where each row of G
ˆ1|=b0 is close to 0.
Use a Wald statistic to assess if g
By the information matrix equality,
ˆ1|=b0 ]  E[H1|=b0 ] where H is the Hessian.
Var[g
Estimate this with the sum of squares as usual:
ˆ ˆ
ˆ 1|=b  Ni=1g

ˆ
ˆ
H
g

G'G
(1|

=b
),i
(1|

=b
),i
0
0
0
Part 2: Basic Econometrics [ 49/51]
LM Test (Cont.)
ˆ1| =b0
Wald statistic = g
-1
ˆ 1|=b  g
H
ˆ1|=b0
0 

-1
ˆ G'G
ˆ ˆ  G'i
ˆ
= iG



-1

ˆ G'G
ˆ ˆ  G'i
ˆ /N
= N  iG


= NR 2
R 2 = the R 2 in the 'uncentered' regression of
a column of ones on the vector of first derivatives.
(N is equal to the total sum of squares.)
This is a general result. We did not assume any
specific model in the derivation. (We did assume
independent observations.)
Part 2: Basic Econometrics [ 50/51]
Representing Covariation


Conditional mean function: E[y | x] = g(x)
Linear approximation to the conditional mean
function: Linear Taylor series
ˆ
g( x ) = g( x 0 ) + ΣKk=1 [gk | x = x 0 ](x k -x k0 )
= 0 + ΣKk=1k (x k -x k0 )

The linear projection (linear regression?)
g*(x)= 0  Kk 1  k (x k -E[x k ])
0
 E[y]
Var[x]}-1 {Cov[x ,y]}
Part 2: Basic Econometrics [ 51/51]
Projection and Regression

The linear projection is not the regression, and is
not the Taylor series.
Example: f(y|x)=[1/λ(x)]exp[-y/λ(x)]
λ(x)=exp(+x)=E[y|x]
x~U[0,1]; f(x)=1, 0  x  1
Part 2: Basic Econometrics [ 52/51]
For the Example: α=1, β=2
20.88
Conditional Mean
16.70
Linear
Projection
Linear Projection
Variable
12.53
Taylor Series
8.35
4.18
.00
.00
.20
.40
.60
.80
X
EY_X
PROJECTN
TAYLOR
1.00