Econometrics-I-19

# Kids 13093 04708 2781 0054 15905 variable coefficient

This preview shows pages 8–15. Sign up to view the full content.

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 --------+------------------------------------------------------------- ™  7/29

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Part 19: MLE Applications Exponential Regression Model ™  8/29 2 1 1 2 1 1 1 1 ( | ) exp( / ), exp( ) [ | ]; [ | ] log ( | ) log log log 1 1 lo Note since [ | ], E i i i i i i i i i i i i n n i i i i i i i n n n i i i i i i i i i i i i i i i i i P y y E y Var y y LogL P y L y y L E y = = = = = = - θ θ θ = = = θ = = - θ - θ ∂θ - = = + θ = - ÷ ÷ ∂θ θ θ θ θ = x x x x x x x x β β β g L = 0 β
Part 19: MLE Applications Variance of the First Derivative ™  9/29 1 1 1 1 ( | ) exp( / ), log 1 log Note since [ | ], E log 1 1 Var [ | ] i i i i i n i i i i i i i n n i i i i i i i i i i i P y y y L L E y L Var y = = = = - θ θ = - ÷ θ θ = = = = θ = ÷ ÷ θ θ 2 2 2 x x x 0 x x x x x X X β β β

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Part 19: MLE Applications Hessian ™  10/29 1 2 2 1 1 2 1 ( | ) exp( / ), log 1 log log because E[ | ] i i i i i n i i i i n n i i i i i i i i i i i i i i P y y y L y y L L E y = = = = - θ θ = - ÷ θ = - θ = - ÷ ÷ ∂ ∂ θ θ - = = θ ∂ ∂ x x x x x x X X, x β β β β β
Part 19: MLE Applications Variance Estimators ™  11/29 1 1 2 Negative inverse of actual second derivatives Matrix ˆ ˆ , exp ˆ Negative inverse of expected second derivatives log Sum of outer n i i i i MLE i i i y L E - = θ = ÷ ÷ θ - = ∂ ∂ -1 x x x X X, so [X X] β β β 1 1 1 1 1 1 products of first derivatives (BHHH) 1 ˆ "Robust" estimator in wide use 1 ˆ ˆ ˆ n i i i i i n n n i i i i i i i i i i i i i i i y y y y - = - = = = - ÷ ÷ θ - ÷ ÷ ÷ ÷ ÷ ÷ θ θ θ 2 2 x x x x x x x x 1 -

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Part 19: MLE Applications Income Data Frequency HHNINC .0 0 0 .4 38 .8 7 6 1.314 1.7 5 3 2 .19 1 2 .6 2 9 3 .0 67 ™  12/29
Part 19: MLE Applications 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***

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

### What students are saying

• As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

Kiran Temple University Fox School of Business ‘17, Course Hero Intern

• I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

Dana University of Pennsylvania ‘17, Course Hero Intern

• The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

Jill Tulane University ‘16, Course Hero Intern