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Part 19: MLE Applications 19-1/31 Econometrics I Professor William Greene Stern School of Business Department of Economics.

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Presentation on theme: "Part 19: MLE Applications 19-1/31 Econometrics I Professor William Greene Stern School of Business Department of Economics."— Presentation transcript:

1 Part 19: MLE Applications 19-1/31 Econometrics I Professor William Greene Stern School of Business Department of Economics

2 Part 19: MLE Applications 19-2/31 Econometrics I Part 19 –MLE Applications and a Two Step Estimator

3 Part 19: MLE Applications 19-3/31 Model for a Binary Dependent Variable  Binary outcome. Event occurs or doesn’t (e.g., the democrat wins, the person enters the labor force,… Model the probability of the event. P(x)=Prob(y=1|x) Probability responds to independent variables  Requirements 0 < Probability < 1 P(x) should be monotonic in x – it’s a CDF

4 Part 19: MLE Applications 19-4/31 Two Standard Models  Based on the normal distribution: Prob[y=1|x] =  (β’x) = CDF of normal distribution The “probit” model  Based on the logistic distribution Prob[y=1|x] = exp(β’x)/[1+ exp(β’x)] The “logit” model  Log likelihood P(y|x) = (1-F) (1-y) F y where F = the cdf LogL = Σ i (1-y i )log(1-F i ) + y i logF i = Σ i F[(2y i -1)β’x] since F(-t)=1-F(t) for both.

5 Part 19: MLE Applications 19-5/31 Coefficients in the Binary Choice Models E[y|x] = 0*(1-F i ) + 1*F i = P(y=1|x) = F(β’x) The coefficients are not the slopes, as usual in a nonlinear model ∂E[y|x]/∂x= f(β’x)β These will look similar for probit and logit

6 Part 19: MLE Applications 19-6/31 Application: Female Labor Supply 1975 Survey Data: Mroz (Econometrica) 753 Observations Descriptive Statistics Variable Mean Std.Dev. Minimum Maximum Cases Missing ============================================================================== All observations in current sample --------+--------------------------------------------------------------------- LFP |.568393.495630.000000 1.00000 753 0 WHRS | 740.576 871.314.000000 4950.00 753 0 KL6 |.237716.523959.000000 3.00000 753 0 K618 | 1.35325 1.31987.000000 8.00000 753 0 WA | 42.5378 8.07257 30.0000 60.0000 753 0 WE | 12.2869 2.28025 5.00000 17.0000 753 0 WW | 2.37457 3.24183.000000 25.0000 753 0 RPWG | 1.84973 2.41989.000000 9.98000 753 0 HHRS | 2267.27 595.567 175.000 5010.00 753 0 HA | 45.1208 8.05879 30.0000 60.0000 753 0 HE | 12.4914 3.02080 3.00000 17.0000 753 0 HW | 7.48218 4.23056.412100 40.5090 753 0 FAMINC | 23080.6 12190.2 1500.00 96000.0 753 0 KIDS |.695883.460338.000000 1.00000 753 0

7 Part 19: MLE Applications 19-7/31 ---------------------------------------------------------------------- Binomial Probit Model Dependent variable LFP Log likelihood function -488.26476 (Probit) Log likelihood function -488.17640 (Logit) --------+------------------------------------------------------------- Variable| Coefficient Standard Error b/St.Er. P[|Z|>z] Mean of X --------+------------------------------------------------------------- |Index function for probability Constant|.77143.52381 1.473.1408 WA| -.02008.01305 -1.538.1241 42.5378 WE|.13881***.02710 5.122.0000 12.2869 HHRS| -.00019**.801461D-04 -2.359.0183 2267.27 HA| -.00526.01285 -.410.6821 45.1208 HE| -.06136***.02058 -2.982.0029 12.4914 FAMINC|.00997**.00435 2.289.0221 23.0806 KIDS| -.34017***.12556 -2.709.0067.69588 --------+------------------------------------------------------------- Binary Logit Model for Binary Choice --------+------------------------------------------------------------- |Characteristics in numerator of Prob[Y = 1] Constant| 1.24556.84987 1.466.1428 WA| -.03289.02134 -1.542.1232 42.5378 WE|.22584***.04504 5.014.0000 12.2869 HHRS| -.00030**.00013 -2.326.0200 2267.27 HA| -.00856.02098 -.408.6834 45.1208 HE| -.10096***.03381 -2.986.0028 12.4914 FAMINC|.01727**.00752 2.298.0215 23.0806 KIDS| -.54990***.20416 -2.693.0071.69588 --------+------------------------------------------------------------- Estimated Choice Models for Labor Force Participation

8 Part 19: MLE Applications 19-8/31 Partial Effects ---------------------------------------------------------------------- Partial derivatives of probabilities with respect to the vector of characteristics. They are computed at the means of the Xs. Observations used are All Obs. --------+------------------------------------------------------------- Variable| Coefficient Standard Error b/St.Er. P[|Z|>z] Elasticity --------+------------------------------------------------------------- |PROBIT: Index function for probability WA| -.00788.00512 -1.538.1240 -.58479 WE|.05445***.01062 5.127.0000 1.16790 HHRS|-.74164D-04**.314375D-04 -2.359.0183 -.29353 HA| -.00206.00504 -.410.6821 -.16263 HE| -.02407***.00807 -2.983.0029 -.52488 FAMINC|.00391**.00171 2.289.0221.15753 |Marginal effect for dummy variable is P|1 - P|0. KIDS| -.13093***.04708 -2.781.0054 -.15905 Variable| Coefficient Standard Error b/St.Er. P[|Z|>z] Elasticity --------+------------------------------------------------------------- |LOGIT: Marginal effect for variable in probability WA| -.00804.00521 -1.542.1231 -.59546 WE|.05521***.01099 5.023.0000 1.18097 HHRS|-.74419D-04**.319831D-04 -2.327.0200 -.29375 HA| -.00209.00513 -.408.6834 -.16434 HE| -.02468***.00826 -2.988.0028 -.53673 FAMINC|.00422**.00184 2.301.0214.16966 |Marginal effect for dummy variable is P|1 - P|0. KIDS| -.13120***.04709 -2.786.0053 -.15894 --------+-------------------------------------------------------------

9 Part 19: MLE Applications 19-9/31 I have a question. The question is as follows. We have a probit model. We used LM tests to test for the hetercodeaticiy in this model and found that there is heterocedasticity in this model... How do we proceed now? What do we do to get rid of heterescedasticiy? Testing for heteroscedasticity in a probit model and then getting rid of heteroscedasticit in this model is not a common procedure. In fact I do not remember seen an applied (or theoretical also) works which tests for heteroscedasticiy and then uses a method to get rid of it??? See Econometric Analysis, 7 th ed. pages 714-714

10 Part 19: MLE Applications 19-10/31 Exponential Regression Model

11 Part 19: MLE Applications 19-11/31 Variance of the First Derivative

12 Part 19: MLE Applications 19-12/31 Hessian

13 Part 19: MLE Applications 19-13/31 Variance Estimators

14 Part 19: MLE Applications 19-14/31 Income Data

15 Part 19: MLE Applications 19-15/31 Exponential Regression --> logl ; lhs=hhninc ; rhs = x ; model=exp $ Normal exit: 11 iterations. Status=0. F= -1550.075 ---------------------------------------------------------------------- Exponential (Loglinear) Regression Model Dependent variable HHNINC Log likelihood function 1550.07536 Restricted log likelihood 1195.06953 Chi squared [ 5 d.f.] 710.01166 Significance level.00000 McFadden Pseudo R-squared -.2970587 Estimation based on N = 27322, K = 6 --------+------------------------------------------------------------- Variable| Coefficient Standard Error b/St.Er. P[|Z|>z] Mean of X --------+------------------------------------------------------------- |Parameters in conditional mean function Constant| 1.77430***.04501 39.418.0000 AGE|.00205***.00063 3.274.0011 43.5272 EDUC| -.05572***.00271 -20.539.0000 11.3202 MARRIED| -.26341***.01568 -16.804.0000.75869 HHKIDS|.06512***.01399 4.657.0000.40272 FEMALE| -.00542.01234 -.439.6603.47881 --------+------------------------------------------------------------- Note: ***, **, * = Significance at 1%, 5%, 10% level. ----------------------------------------------------------------------

16 Part 19: MLE Applications 19-16/31 Variance Estimators histogram;rhs=hhninc$ reject ; hhninc=0$ namelist ; X = one, age,educ,married,hhkids,female $ loglinear ; lhs=hhninc ; rhs = x ; model=exp $ create ; thetai = exp(b'x) $ create ; gi = (hhninc/thetai - 1) ; gi2 = gi^2 $$ create ; hi = (hhninc/thetai) $ matrix ; Expected = ; Stat(b,Expected,X)$ matrix ; Actual = ; Stat(b,Actual,X) $ matrix ; BHHH = ; Stat(b,BHHH,X) $ matrix ; Robust = Actual * X'[gi2]X * Actual ; Stat(b,Robust,X) $

17 Part 19: MLE Applications 19-17/31 Estimates --------+-------------------------------------------------- Variable| Coefficient Standard Error b/St.Er. P[|Z|>z] --------+-------------------------------------------------- --> matrix ; Expected = ; Stat(b,Expected,X)$ Constant| 1.77430***.04548 39.010.0000 AGE|.00205***.00061 3.361.0008 EDUC| -.05572***.00269 -20.739.0000 MARRIED| -.26341***.01558 -16.902.0000 HHKIDS|.06512***.01425 4.571.0000 FEMALE| -.00542.01235 -.439.6605 --> matrix ; Actual = ; Stat(b,Actual,X) $ Constant| 1.77430***.11922 14.883.0000 AGE|.00205.00181 1.137.2553 EDUC| -.05572***.00631 -8.837.0000 MARRIED| -.26341***.04954 -5.318.0000 HHKIDS|.06512*.03920 1.661.0967 FEMALE| -.00542.03471 -.156.8759 --> matrix ; BHHH = ; Stat(b,BHHH,X) $ Constant| 1.77430***.05409 32.802.0000 AGE|.00205***.00069 2.973.0029 EDUC| -.05572***.00331 -16.815.0000 MARRIED| -.26341***.01737 -15.165.0000 HHKIDS|.06512***.01637 3.978.0001 FEMALE| -.00542.01410 -.385.7004 --> matrix ; Robust = Actual * X'[gi2]X * Actual $ Constant| 1.77430***.28500 6.226.0000 AGE|.00205.00481.427.6691 EDUC| -.05572***.01306 -4.268.0000 MARRIED| -.26341*.14581 -1.806.0708 HHKIDS|.06512.09459.689.4911 FEMALE| -.00542.08580 -.063.9496

18 Part 19: MLE Applications 19-18/31 GARCH Models: A Model for Time Series with Latent Heteroscedasticity Bollerslev/Ghysel, 1974

19 Part 19: MLE Applications 19-19/31 ARCH Model

20 Part 19: MLE Applications 19-20/31 GARCH Model

21 Part 19: MLE Applications 19-21/31 Estimated GARCH Model ---------------------------------------------------------------------- GARCH MODEL Dependent variable Y Log likelihood function -1106.60788 Restricted log likelihood -1311.09637 Chi squared [ 2 d.f.] 408.97699 Significance level.00000 McFadden Pseudo R-squared.1559676 Estimation based on N = 1974, K = 4 GARCH Model, P = 1, Q = 1 Wald statistic for GARCH = 3727.503 --------+------------------------------------------------------------- Variable| Coefficient Standard Error b/St.Er. P[|Z|>z] Mean of X --------+------------------------------------------------------------- |Regression parameters Constant| -.00619.00873 -.709.4783 |Unconditional Variance Alpha(0)|.01076***.00312 3.445.0006 |Lagged Variance Terms Delta(1)|.80597***.03015 26.731.0000 |Lagged Squared Disturbance Terms Alpha(1)|.15313***.02732 5.605.0000 |Equilibrium variance, a0/[1-D(1)-A(1)] EquilVar|.26316.59402.443.6577 --------+-------------------------------------------------------------

22 Part 19: MLE Applications 19-22/31 2 Step Estimation (Murphy-Topel) Setting, fitting a model which contains parameter estimates from another model. Typical application, inserting a prediction from one model into another. A. Procedures: How it's done. B. Asymptotic results: 1. Consistency 2. Getting an appropriate estimator of the asymptotic covariance matrix The Murphy - Topel result Application: Equation 1: Number of children Equation 2: Labor force participation

23 Part 19: MLE Applications 19-23/31 Setting  Two equation model: Model for y 1 = f(y 1 | x 1, θ 1 ) Model for y 2 = f(y 2 | x 2, θ 2, x 1, θ 1 ) (Note, not ‘simultaneous’ or even ‘recursive.’)  Procedure: Estimate θ 1 by ML, with covariance matrix (1/n)V 1 Estimate θ 2 by ML treating θ 1 as if it were known. Correct the estimated asymptotic covariance matrix, (1/n)V 2 for the estimator of θ 2

24 Part 19: MLE Applications 19-24/31 Murphy and Topel (1984,2002) Results Both MLEs are consistent

25 Part 19: MLE Applications 19-25/31 M&T Computations

26 Part 19: MLE Applications 19-26/31 Example Equation 1: Number of Kids – Poisson Regression p(y i1 |x i1, β)=exp(-λ i )λ i yi1 /y i1 ! λ i = exp(x i1 ’β) g i1 = x i1 (y i1 – λ i ) V 1 = [(1/n)Σ(-λ i )x i1 x i1 ’] -1

27 Part 19: MLE Applications 19-27/31 Example - Continued Equation 2: Labor Force Participation – Logit p(y i2 |x i2,δ,α,x i1,β)=exp(d i2 )/[1+exp(d i2 )]=P i2 d i2 = (2y i2 -1)[δx i2 + αλ i ] λ i = exp(βx i1 ) Let z i2 = (x i2, λ i ), θ 2 = (δ, α) d i2 = (2y i2 -1)[θ 2 z i2 ] g i2 = (y i2 -P i2 )z i2 V 2 = [(1/n)Σ{-P i2 (1-P i2 )}z i2 z i2 ’] -1

28 Part 19: MLE Applications 19-28/31 Murphy and Topel Correction

29 Part 19: MLE Applications 19-29/31 Two Step Estimation of LFP Model ? Data transformations. Number of kids, scale income variables Create ; Kids = kl6 + k618 ; income = faminc/10000 ; Wifeinc = ww*whrs/1000 $ ? Equation 1, number of kids. Standard Poisson fertility model. ? Fit equation, collect parameters BETA and covariance matrix V1 ? Then compute fitted values and derivatives Namelist ; X1 = one,wa,we,income,wifeinc$ Poisson ; Lhs = kids ; Rhs = X1 $ Matrix ; Beta = b ; V1 = N*VARB $ Create ; Lambda = Exp(X1'Beta); gi1 = Kids - Lambda $ ? Set up logit labor force participation model ? Compute probit model and collect results. Delta=Coefficients on X2 ? Alpha = coefficient on fitted number of kids. Namelist ; X2 = one,wa,we,ha,he,income ; Z2 = X2,Lambda $ Logit ; Lhs = lfp ; Rhs = Z2 $ Calc ; alpha = b(kreg) ; K2 = Col(X2) $ Matrix ; delta=b(1:K2) ; Theta2 = b ; V2 = N*VARB $ ? Poisson derivative of with respect to beta is (kidsi - lambda)´X1 Create ; di = delta'X2 + alpha*Lambda ; pi2= exp(di)/(1+exp(di)) ; gi2 = LFP - Pi2 ? These are the terms that are used to compute R and C. ; ci = gi2*gi2*alpha*lambda ; ri = gi2*gi1$ MATRIX ; C = 1/n*Z2'[ci]X1 ; R = 1/n*Z2'[ri]X1 ; A = C*V1*C' - R*V1*C' - C*V1*R' ; V2S = V2+V2*A*V2 ; V2s = 1/N*V2S $ ? Compute matrix products and report results Matrix ; Stat(Theta2,V2s,Z2)$

30 Part 19: MLE Applications 19-30/31 Estimated Equation 1: E[Kids] +---------------------------------------------+ | Poisson Regression | | Dependent variable KIDS | | Number of observations 753 | | Log likelihood function -1123.627 | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+----------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | Mean of X| +---------+--------------+----------------+--------+---------+----------+ Constant 3.34216852.24375192 13.711.0000 WA -.06334700.00401543 -15.776.0000 42.5378486 WE -.02572915.01449538 -1.775.0759 12.2868526 INCOME.06024922.02432043 2.477.0132 2.30805950 WIFEINC -.04922310.00856067 -5.750.0000 2.95163126

31 Part 19: MLE Applications 19-31/31 Two Step Estimator +---------------------------------------------+ | Multinomial Logit Model | | Dependent variable LFP | | Number of observations 753 | | Log likelihood function -351.5765 | | Number of parameters 7 | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+----------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | Mean of X| +---------+--------------+----------------+--------+---------+----------+ Characteristics in numerator of Prob[Y = 1] Constant 33.1506089 2.88435238 11.493.0000 WA -.54875880.05079250 -10.804.0000 42.5378486 WE -.02856207.05754362 -.496.6196 12.2868526 HA -.01197824.02528962 -.474.6358 45.1208499 HE -.02290480.04210979 -.544.5865 12.4913679 INCOME.39093149.09669418 4.043.0001 2.30805950 LAMBDA -5.63267225.46165315 -12.201.0000 1.59096946 With Corrected Covariance Matrix +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ Constant 33.1506089 5.41964589 6.117.0000 WA -.54875880.07780642 -7.053.0000 WE -.02856207.12508144 -.228.8194 HA -.01197824.02549883 -.470.6385 HE -.02290480.04862978 -.471.6376 INCOME.39093149.27444304 1.424.1543 LAMBDA -5.63267225 1.07381248 -5.245.0000


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