.
.
. ********** MEMORY MANAGEMENT
. set maxvar 100 width 1000
(maxvar and maxobs no longer need be set with this version of Stata)
.
.
. ********** READ DATA
. * See program stjaggia.asc for explanation of the following command
. * You need files jaggia.asc and jaggia.dct in your directory
. infile using jaggia.dct
dictionary using jaggia.asc {
_column(1) docno %16.8f "Document Number"
_column(17) weeks %17.8f "Weeks"
_column(34) numbids %17.8f "Number of takeover bids (after the first)"
_column(51) takeover %17.8f "1 if takeover occurred, 0 otherwise"
_newline
_column(1) bidprem %16.8f "Bid price / price 14 work days before bid"
_column(17) insthold %17.8f "Percentage of stock held by insitutions"
_column(34) size %17.8f "Total book value of assets inb $billions "
_column(51) leglrest %17.8f "Equals one if legal defense by lawsuit"
_newline
_column(1) realrest %16.8f "One if proposed changes in asset structure"
_column(17) finrest %17.8f "One if proposed changes in ownership struc"
_column(34) regulatn %17.8f "One if intervention by fed regulators"
_column(57) whtknght %17.8f "One if mgmt invite friendly 3rd-party bid"
* For this example the column numbers are redundant.
* Also the long labels need not be given.
}
(126 observations read)
.
.
. ********** DATA TRANSFORMATIONS
. gen sizesq = size*size
. label variable sizesq "size squared"
.
.
. ******** CHECK DATA: DESCRIPTIVE STATISTICS
. describe
Contains data
obs: 126
vars: 13
size: 7,056 (99.2% of memory free)
-------------------------------------------------------------------------------
1. docno float %9.0g Document Number
2. weeks float %9.0g Weeks
3. numbids float %9.0g Number of takeover bids (after
the first)
4. takeover float %9.0g 1 if takeover occurred, 0
otherwise
5. bidprem float %9.0g Bid price / price 14 work days
before bid
6. insthold float %9.0g Percentage of stock held by
insitutions
7. size float %9.0g Total book value of assets inb
$billions
8. leglrest float %9.0g Equals one if legal defense by
lawsuit
9. realrest float %9.0g One if proposed changes in
asset structure
10. finrest float %9.0g One if proposed changes in
ownership struc
11. regulatn float %9.0g One if intervention by fed
regulators
12. whtknght float %9.0g One if mgmt invite friendly
3rd-party bid
13. sizesq float %9.0g size squared
-------------------------------------------------------------------------------
Sorted by:
Note: dataset has changed since last saved
. summarize
Variable | Obs Mean Std. Dev. Min Max
---------+-----------------------------------------------------
docno | 126 82174.41 2251.783 78001 85059
weeks | 126 11.44898 7.711424 2.857 41.429
numbids | 126 1.738095 1.432081 0 10
takeover | 126 1 0 1 1
bidprem | 126 1.346806 .189325 .9426754 2.066366
insthold | 126 .2518175 .1856136 0 .904
size | 126 1.219031 3.096624 .017722 22.169
leglrest | 126 .4285714 .4968472 0 1
realrest | 126 .1825397 .3878308 0 1
finrest | 126 .1031746 .3054011 0 1
regulatn | 126 .2698413 .4456492 0 1
whtknght | 126 .5952381 .4928054 0 1
sizesq | 126 10.99902 59.91479 .0003141 491.4646
.
.
. ********** POISSON REGRESSION
. *
. * Use /* and */ as command spans two lines
.
. poisson numbids leglrest realrest finrest whtknght /*
> */ bidprem insthold size sizesq regulatn
Iteration 0: log likelihood = -184.9518
Iteration 1: log likelihood = -184.94833
Iteration 2: log likelihood = -184.94833
Poisson regression Number of obs = 126
LR chi2(9) = 33.25
Prob > chi2 = 0.0001
Log likelihood = -184.94833 Pseudo R2 = 0.0825
------------------------------------------------------------------------------
numbids | Coef. Std. Err. z P>|z| [95% Conf. Interval]
---------+--------------------------------------------------------------------
leglrest | .2601464 .1509594 1.723 0.085 -.0357286 .5560213
realrest | -.1956597 .1926309 -1.016 0.310 -.5732093 .1818899
finrest | .07403 .2165219 0.342 0.732 -.3503452 .4984053
whtknght | .4813822 .1588698 3.030 0.002 .170003 .7927613
bidprem | -.6776959 .3767373 -1.799 0.072 -1.416087 .0606956
insthold | -.3619913 .4243292 -0.853 0.394 -1.193661 .4696788
size | .1785026 .0600221 2.974 0.003 .0608614 .2961438
sizesq | -.0075693 .0031217 -2.425 0.015 -.0136878 -.0014509
regulatn | -.0294392 .1605682 -0.183 0.855 -.344147 .2852686
_cons | .9860599 .5339202 1.847 0.065 -.0604044 2.032524
------------------------------------------------------------------------------
.
.
. ********** MAXIMUM LIKELIHOOD FOR POISSON
.
. * Based on Weibull example in [R] ml - maximum likelihood
. * See especially Stata 6 Manual Reference H-O p.379 and p.388-399
. * The possible methods are lf, d0, d1 and d2.
. * Here I use d0, d1 and d2 which give log-density etc for each observation.
. * I do not use lf which gives the log-likeihood over all observations
.
. * The general form for the command is
. * (1) Define the program, log-density, perhaps gradient and perhaps hessian
>
. * program define myprog
. * : :
. * end
. * (2) Run the program providing dependent variable(s) and regressor(s)
. * ml model d*** myprog (dep1 dep2 ... = x's for eqn1) (x's for eqn2) ..
>
. * ml maximize
. * where d*** called by d0, d1, d1debug, d2 and d2debug
. * and the debug variants check anayltical against numerical derivatives
. * and for robust standard errors based on A-in*B*A-inv add , robust
.
.
. * For the Poisson log-density
. * ln L = Sum_i {-mu_i + y_i*ln(mu_i) - ln(y_i!)}
. * where
. * mu_i = exp(x_i'b)
. *
. * The first-order conditions are
. * Sum_i (y_i - exp(x_i'b))x_i = 0
. *
. * and the second-derivative matrix is
. * Sum_i -exp(x_i'b))*x_i*x_i'
.
.
. * (1) Define the program, log-density, perhaps gradient and perhaps hessian
>
. * The Poisson model is simple as only one index and one dependent variable
. * There is one dependent variable y which is stored in $ML_y1
. * There is one index which is x'b and for the Poisson model is mu i.e. E[y|x]
>
. * For log-density -mu_i + y_i*ln(mu_i) - ln(y_i!) where mu_i = exp(x_i'b)
. * For gradient d/db = (y_i - mu_i)*x_i
. * but Stata wands d/dtheta_i = (y_i - mu_i) using theta_i = x_i'b
. * For hessian mu_i*x_i*x_i'
. * but Stata wants d2/dtheta_i^2 = mu_i using theta_i = x_i'b
.
. * (2) Example is
. * ml model d1debug mypois1 (numbids = bidprem)
. * ml maximize
.
. * For program debuggin which produces much output
. * set trace on
.
. * The following programs are given
. * mypois0 use numerical derivatives throughout
. * mypois1 use analytical first derivative and numerical second
. * mypois1r same as mypois1 but form robust variance matrix
. * mypois2 use analytical first and second derivative
. * mypois1 same as mypois1 but form robust variance matrix
.
.
. ******* (1) MYPOIS0 PROGRAM: POISSON WITH NUMERICAL DERIVATIVES THROUGHOUT
.
. program define mypois0
1. version 6.0
2. args todo b lnf /* Need to use the names todo b and lnf
> todo always contains 1 and may be ignored
> b is parameters and lnf is log-density */
3. tempvar theta1 /* create as needed to calculate lf, g, ... */
4. mleval `theta1' = `b', eq(1) /* theta1 is theta1_i = x_i'b */
5. local y "$ML_y1" /* create to make program more readable */
6. tempvar lnyfact mu
7. quietly gen double `lnyfact' = lnfact(`y')
8. quietly gen double `mu' = exp(`theta1')
9. mlsum `lnf' = -`mu' + `y'*ln(`mu') - `lnyfact'
10. end
.
. ******* (1) OUTPUT: POISSON WITH NUMERICAL DERIVATIVES THROUGHOUT
.
. ml model d0 mypois0 (numbids = leglrest realrest finrest whtknght /*
> */ bidprem insthold size sizesq regulatn)
. ml maximize
initial: log likelihood = -229.63257
alternative: log likelihood = -201.87145
rescale: log likelihood = -201.87145
Iteration 0: log likelihood = -201.87145
Iteration 1: log likelihood = -187.1169
Iteration 2: log likelihood = -184.95692
Iteration 3: log likelihood = -184.94833
Iteration 4: log likelihood = -184.94833
Number of obs = 126
Wald chi2(9) = 33.79
Log likelihood = -184.94833 Prob > chi2 = 0.0001
------------------------------------------------------------------------------
numbids | Coef. Std. Err. z P>|z| [95% Conf. Interval]
---------+--------------------------------------------------------------------
leglrest | .2601464 .1509594 1.723 0.085 -.0357286 .5560214
realrest | -.1956597 .192631 -1.016 0.310 -.5732096 .1818901
finrest | .07403 .216522 0.342 0.732 -.3503452 .4984052
whtknght | .4813821 .1588698 3.030 0.002 .1700029 .7927613
bidprem | -.6776959 .3767378 -1.799 0.072 -1.416088 .0606967
insthold | -.3619913 .4243294 -0.853 0.394 -1.193662 .4696792
size | .1785026 .0600224 2.974 0.003 .0608608 .2961444
sizesq | -.0075694 .0031217 -2.425 0.015 -.0136878 -.0014509
regulatn | -.0294392 .1605682 -0.183 0.855 -.3441471 .2852686
_cons | .98606 .5339209 1.847 0.065 -.0604058 2.032526
------------------------------------------------------------------------------
.
.
. ******* (2) MYPOIS1 PROGRAM: POISSON WITH ANAYLTICAL FIRST DERIVATIVES
.
. program define mypois1
1. version 6.0
2. args todo b lnf g /* Need to use the names todo b and lnf
> todo always contains 1 and may be ignored
> b is parameters, lnf is log-density
> g is gradient */
3. tempvar theta1 /* create as needed to calculate lf, g, ... */
4. mleval `theta1' = `b', eq(1) /* theta1 is theta1_i = x_i'b */
5. local y "$ML_y1" /* create to make program more readable */
6. tempvar lnyfact mu
7. quietly gen double `lnyfact' = lnfact(`y')
8. quietly gen double `mu' = exp(`theta1')
9. mlsum `lnf' = -`mu' + `y'*ln(`mu') - `lnyfact'
10. /* Following code is extra for analytical first derivatives */
. if `todo'==0 | `lnf'==. {exit}
11. tempname d1
12. mlvecsum `lnf' `d1' = `y' - `mu'
13. matrix `g' = `d1'
14. end
.
. ******* (2) MYPOIS1 OUTPUT: POISSON WITH ANALYTICAL FIRST DERIVATIVES
.
. ml model d1debug mypois1 (numbids = leglrest realrest finrest whtknght /*
> */ bidprem insthold size sizesq regulatn)
. ml maximize
initial: log likelihood = -229.63257
alternative: log likelihood = -201.87145
rescale: log likelihood = -201.87145
d1debug: Begin derivative-comparison report ---------------------------------
d1debug: mreldif(gradient vector) = .0011843
d1debug: End derivative-comparison report -----------------------------------
Iteration 0: log likelihood = -201.87145
d1debug: Begin derivative-comparison report ---------------------------------
d1debug: mreldif(gradient vector) = 6.29e-09
d1debug: End derivative-comparison report -----------------------------------
Iteration 1: log likelihood = -187.1169
d1debug: Begin derivative-comparison report ---------------------------------
d1debug: mreldif(gradient vector) = 6.83e-07
d1debug: End derivative-comparison report -----------------------------------
Iteration 2: log likelihood = -184.95692
d1debug: Begin derivative-comparison report ---------------------------------
d1debug: mreldif(gradient vector) = .0003457
d1debug: End derivative-comparison report -----------------------------------
Iteration 3: log likelihood = -184.94833
d1debug: Begin derivative-comparison report ---------------------------------
d1debug: mreldif(gradient vector) = .0004541
d1debug: End derivative-comparison report -----------------------------------
Iteration 4: log likelihood = -184.94833
Number of obs = 126
Wald chi2(9) = 33.79
Log likelihood = -184.94833 Prob > chi2 = 0.0001
------------------------------------------------------------------------------
numbids | Coef. Std. Err. z P>|z| [95% Conf. Interval]
---------+--------------------------------------------------------------------
leglrest | .2601464 .1509594 1.723 0.085 -.0357286 .5560214
realrest | -.1956597 .192631 -1.016 0.310 -.5732096 .1818901
finrest | .07403 .216522 0.342 0.732 -.3503452 .4984052
whtknght | .4813821 .1588698 3.030 0.002 .1700029 .7927613
bidprem | -.6776959 .3767378 -1.799 0.072 -1.416088 .0606967
insthold | -.3619913 .4243294 -0.853 0.394 -1.193662 .4696792
size | .1785026 .0600224 2.974 0.003 .0608608 .2961444
sizesq | -.0075694 .0031217 -2.425 0.015 -.0136878 -.0014509
regulatn | -.0294392 .1605682 -0.183 0.855 -.3441471 .2852686
_cons | .98606 .5339209 1.847 0.065 -.0604058 2.032526
------------------------------------------------------------------------------
.
.
. ******* (3) MYPOIS1r PROGRAM: ROBUST SE'S FOR POISSON ANAYLTICAL FIRST
.
. program define mypois1r
1. version 6.0
2. args todo b lnf g negH g1 /* Need to use the names todo b and lnf
> todo always contains 1 and may be ignored
> b is parameters, lnf is log-density
> g is gradient negH need not be provided here
> g1 is to be used for robust se's */
3. tempvar theta1 /* create as needed to calculate lf, g, ... */
4. mleval `theta1' = `b', eq(1) /* theta1 is theta1_i = x_i'b */
5. local y "$ML_y1" /* create to make program more readable */
6. tempvar lnyfact mu
7. quietly gen double `lnyfact' = lnfact(`y')
8. quietly gen double `mu' = exp(`theta1')
9. mlsum `lnf' = -`mu' + `y'*ln(`mu') - `lnyfact'
10. /* Following code is extra for analytical first derivatives
> and also changed due to robust se's */
. if `todo'==0 | `lnf'==. {exit}
11. tempname d1
12. quietly replace `g1' = `y' - `mu'
13. mlvecsum `lnf' `d1' = `g1'
14. matrix `g' = `d1'
15. end
.
. ******* (3) MYPOIS1r OUTPUT: ROBUST SE'S FOR POISSON ANAYLTICAL FIRST
.
. ml model d1debug mypois1r (numbids = leglrest realrest finrest whtknght /*
> */ bidprem insthold size sizesq regulatn), robust
. ml maximize
initial: log likelihood = -229.63257
alternative: log likelihood = -201.87145
rescale: log likelihood = -201.87145
d1debug: Begin derivative-comparison report ---------------------------------
d1debug: mreldif(gradient vector) = .0011843
d1debug: End derivative-comparison report -----------------------------------
Iteration 0: log likelihood = -201.87145
d1debug: Begin derivative-comparison report ---------------------------------
d1debug: mreldif(gradient vector) = 6.29e-09
d1debug: End derivative-comparison report -----------------------------------
Iteration 1: log likelihood = -187.1169
d1debug: Begin derivative-comparison report ---------------------------------
d1debug: mreldif(gradient vector) = 6.83e-07
d1debug: End derivative-comparison report -----------------------------------
Iteration 2: log likelihood = -184.95692
d1debug: Begin derivative-comparison report ---------------------------------
d1debug: mreldif(gradient vector) = .0003457
d1debug: End derivative-comparison report -----------------------------------
Iteration 3: log likelihood = -184.94833
d1debug: Begin derivative-comparison report ---------------------------------
d1debug: mreldif(gradient vector) = .0004541
d1debug: End derivative-comparison report -----------------------------------
Iteration 4: log likelihood = -184.94833
Number of obs = 126
Wald chi2(9) = 34.98
Log likelihood = -184.94833 Prob > chi2 = 0.0001
------------------------------------------------------------------------------
| Robust
numbids | Coef. Std. Err. z P>|z| [95% Conf. Interval]
---------+--------------------------------------------------------------------
leglrest | .2601464 .1250534 2.080 0.037 .0150462 .5052465
realrest | -.1956597 .1816168 -1.077 0.281 -.5516222 .1603027
finrest | .07403 .2635711 0.281 0.779 -.4425598 .5906198
whtknght | .4813821 .1064948 4.520 0.000 .2726562 .690108
bidprem | -.6776959 .297425 -2.279 0.023 -1.260638 -.0947536
insthold | -.3619913 .3231802 -1.120 0.263 -.9954127 .2714302
size | .1785026 .062355 2.863 0.004 .056289 .3007161
sizesq | -.0075694 .0027788 -2.724 0.006 -.0130158 -.0021229
regulatn | -.0294392 .1420508 -0.207 0.836 -.3078536 .2489752
_cons | .98606 .4137396 2.383 0.017 .1751453 1.796975
------------------------------------------------------------------------------
.
.
. ******* (4) MYPOIS2 PROGRAM: POISSON WITH ANAYLTICAL SECOND DERIVATIVES
.
. program define mypois2
1. version 6.0
2. args todo b lnf g negH /* Need to use the names todo b and lnf
> todo always contains 1 and may be ignored
> b is parameters, lnf is log-density
> g is gradient, negH is negative hessian */
3. tempvar theta1 /* create as needed to calculate lf, g, ... */
4. mleval `theta1' = `b', eq(1) /* theta1 is theta1_i = x_i'b */
5. local y "$ML_y1" /* create to make program more readable */
6. tempvar lnyfact mu
7. quietly gen double `lnyfact' = lnfact(`y')
8. quietly gen double `mu' = exp(`theta1')
9. mlsum `lnf' = -`mu' + `y'*ln(`mu') - `lnyfact'
10. /* Following code is extra for analytical first derivatives */
. if `todo'==0 | `lnf'==. {exit}
11. tempname d1
12. mlvecsum `lnf' `d1' = `y' - `mu'
13. matrix `g' = `d1'
14. /* Following code is extra for analytical second derivatives */
. if `todo'==0 | `lnf'==. {exit}
15. tempname d11
16. mlmatsum `lnf' `d11' = `mu'
17. matrix `negH' = `d11'
18. end
.
. ******* (4) MYPOIS2 OUTPUT: POISSON WITH ANALYTICAL SECOND DERIVATIVES
.
. ml model d2debug mypois2 (numbids = leglrest realrest finrest whtknght /*
> */ bidprem insthold size sizesq regulatn)
. ml maximize
initial: log likelihood = -229.63257
alternative: log likelihood = -201.87145
rescale: log likelihood = -201.87145
d2debug: Begin derivative-comparison report ---------------------------------
d2debug: mreldif(gradient vector) = .0011843
d2debug: mreldif(negative Hessian) = .0006026
d2debug: End derivative-comparison report -----------------------------------
Iteration 0: log likelihood = -201.87145
d2debug: Begin derivative-comparison report ---------------------------------
d2debug: mreldif(gradient vector) = 6.29e-09
d2debug: mreldif(negative Hessian) = .0419201
d2debug: End derivative-comparison report -----------------------------------
Iteration 1: log likelihood = -187.1169
d2debug: Begin derivative-comparison report ---------------------------------
d2debug: mreldif(gradient vector) = 6.83e-07
d2debug: mreldif(negative Hessian) = 3.48e-06
d2debug: End derivative-comparison report -----------------------------------
Iteration 2: log likelihood = -184.95692
d2debug: Begin derivative-comparison report ---------------------------------
d2debug: mreldif(gradient vector) = .0003457
d2debug: mreldif(negative Hessian) = 6.50e-07
d2debug: End derivative-comparison report -----------------------------------
Iteration 3: log likelihood = -184.94833
d2debug: Begin derivative-comparison report ---------------------------------
d2debug: mreldif(gradient vector) = .0004541
d2debug: mreldif(negative Hessian) = 6.54e-07
d2debug: End derivative-comparison report -----------------------------------
Iteration 4: log likelihood = -184.94833
Number of obs = 126
Wald chi2(9) = 33.79
Log likelihood = -184.94833 Prob > chi2 = 0.0001
------------------------------------------------------------------------------
numbids | Coef. Std. Err. z P>|z| [95% Conf. Interval]
---------+--------------------------------------------------------------------
leglrest | .2601464 .1509594 1.723 0.085 -.0357286 .5560214
realrest | -.1956597 .192631 -1.016 0.310 -.5732096 .1818901
finrest | .07403 .216522 0.342 0.732 -.3503452 .4984052
whtknght | .4813821 .1588698 3.030 0.002 .1700029 .7927613
bidprem | -.6776959 .3767378 -1.799 0.072 -1.416088 .0606967
insthold | -.3619913 .4243294 -0.853 0.394 -1.193662 .4696792
size | .1785026 .0600224 2.974 0.003 .0608608 .2961444
sizesq | -.0075694 .0031217 -2.425 0.015 -.0136878 -.0014509
regulatn | -.0294392 .1605682 -0.183 0.855 -.3441471 .2852686
_cons | .98606 .5339209 1.847 0.065 -.0604058 2.032526
------------------------------------------------------------------------------
.
.
. ******* (5) MYPOIS2r PROGRAM: ROBUST SE'S FOR POISSON ANAYLTICAL SECOND
.
. program define mypois2r
1. version 6.0
2. args todo b lnf g negH g1 /* Need to use the names todo b and lnf
> todo always contains 1 and may be ignored
> b is parameters, lnf is log-density
> g is gradient negH need not be provided here
> g1 is to be used for robust se's */
3. tempvar theta1 /* create as needed to calculate lf, g, ... */
4. mleval `theta1' = `b', eq(1) /* theta1 is theta1_i = x_i'b */
5. local y "$ML_y1" /* create to make program more readable */
6. tempvar lnyfact mu
7. quietly gen double `lnyfact' = lnfact(`y')
8. quietly gen double `mu' = exp(`theta1')
9. mlsum `lnf' = -`mu' + `y'*ln(`mu') - `lnyfact'
10. /* Following code is extra for analytical first derivatives
> and also changed due to robust se's */
. if `todo'==0 | `lnf'==. {exit}
11. tempname d1
12. quietly replace `g1' = `y' - `mu'
13. mlvecsum `lnf' `d1' = `g1'
14. matrix `g' = `d1'
15. /* Following code is extra for analytical second derivatives */
. if `todo'==0 | `lnf'==. {exit}
16. tempname d11
17. mlmatsum `lnf' `d11' = `mu'
18. matrix `negH' = `d11'
19. end
.
. ******* (3) MYPOIS2r OUTPUT: ROBUST SE'S FOR POISSON ANAYLTICAL SECOND
.
. ml model d2debug mypois2r (numbids = leglrest realrest finrest whtknght /*
> */ bidprem insthold size sizesq regulatn), robust
. ml maximize
initial: log likelihood = -229.63257
alternative: log likelihood = -201.87145
rescale: log likelihood = -201.87145
d2debug: Begin derivative-comparison report ---------------------------------
d2debug: mreldif(gradient vector) = .0011843
d2debug: mreldif(negative Hessian) = .0006026
d2debug: End derivative-comparison report -----------------------------------
Iteration 0: log likelihood = -201.87145
d2debug: Begin derivative-comparison report ---------------------------------
d2debug: mreldif(gradient vector) = 6.29e-09
d2debug: mreldif(negative Hessian) = .0419201
d2debug: End derivative-comparison report -----------------------------------
Iteration 1: log likelihood = -187.1169
d2debug: Begin derivative-comparison report ---------------------------------
d2debug: mreldif(gradient vector) = 6.83e-07
d2debug: mreldif(negative Hessian) = 3.48e-06
d2debug: End derivative-comparison report -----------------------------------
Iteration 2: log likelihood = -184.95692
d2debug: Begin derivative-comparison report ---------------------------------
d2debug: mreldif(gradient vector) = .0003457
d2debug: mreldif(negative Hessian) = 6.50e-07
d2debug: End derivative-comparison report -----------------------------------
Iteration 3: log likelihood = -184.94833
d2debug: Begin derivative-comparison report ---------------------------------
d2debug: mreldif(gradient vector) = .0004541
d2debug: mreldif(negative Hessian) = 6.54e-07
d2debug: End derivative-comparison report -----------------------------------
Iteration 4: log likelihood = -184.94833
Number of obs = 126
Wald chi2(9) = 34.98
Log likelihood = -184.94833 Prob > chi2 = 0.0001
------------------------------------------------------------------------------
| Robust
numbids | Coef. Std. Err. z P>|z| [95% Conf. Interval]
---------+--------------------------------------------------------------------
leglrest | .2601464 .1250534 2.080 0.037 .0150462 .5052465
realrest | -.1956597 .1816168 -1.077 0.281 -.5516222 .1603027
finrest | .07403 .2635711 0.281 0.779 -.4425598 .5906198
whtknght | .4813821 .1064948 4.520 0.000 .2726562 .690108
bidprem | -.6776959 .297425 -2.279 0.023 -1.260638 -.0947536
insthold | -.3619913 .3231802 -1.120 0.263 -.9954127 .2714302
size | .1785026 .062355 2.863 0.004 .056289 .3007161
sizesq | -.0075694 .0027788 -2.724 0.006 -.0130158 -.0021229
regulatn | -.0294392 .1420508 -0.207 0.836 -.3078536 .2489752
_cons | .98606 .4137396 2.383 0.017 .1751453 1.796975
------------------------------------------------------------------------------
.
.
. ******* EXTRAS
.
. * For models with two indexes see Weibull code in Stata 6 manual
.
.
. ********** CLOSE OUTPUT
. log close