处理效应模型

一．处理效应模型命令简介

原始命令为：**treatreg has been renamed to etregress.

现在更新为：etregress

etregress语法格式为：

Basic syntax *基础格式

etregress depvar [indepvars], treat(depvar_t = indepvars_t) [twostep|cfunction]

Full syntax for maximum likelihood estimates only ML估计格式

etregress depvar [indepvars] [ if] [ in] [weight], treat(depvar_t = indepvars_t [, noconstant]) [etregress_ml_options]

Full syntax for two-step consistent estimates only\2阶段一致估计\

etregress depvar [indepvars] [ if] [ in], treat(depvar_t = indepvars_t [, noconstant]) twostep [etregress_ts_options]

Full syntax for control-function estimates only控制函数估计格式

etregress depvar [indepvars] [ if] [ in], treat(depvar_t = indepvars_t [, noconstant]) cfunction [etregress_cf_options]

展开全文

选项介绍：

depvar ：结果变量。

indepvars ：直接影响结果变量depvar的自变量。

treat(depvar_t = indepvars_t [, noconstant]) 处理方程

noconstant ：不包含截距项。

constraints(constraints) ：应用线性约束

twostep表示使用两步法，默认MLE

first表示汇报第一阶段的Probit回归结果

depvar ：结果变量。

indepvars ：直接影响结果变量depvar的自变量。

treat(depvar_t = indepvars_t [, noconstant]) 处理方程

noconstant ：不包含截距项。

constraints(constraints) ：应用线性约束

twostep表示使用两步法，默认MLE

first表示汇报第一阶段的Probit回归结果

首先调用数据* union3，*

use union3

数据结构如下：

desc

结果为：

webuse union3

(National Longitudinal Survey. Young Women 14-26 years of age in1968)

. desc

Contains data from http://www.stata-press.com/data/r14/union3.dta

obs: 1,693 National Longitudinal Survey. Young

Women 14-26 years of age in1968

vars: 24 11 Mar 2014 09:47

size: 77,878

--------------------------------------------------------------------------------------

storage display value

variable name typeformat label variable label

--------------------------------------------------------------------------------------

idcode int %8.0g NLS ID

year byte %8.0g interview year

birth_yr byte %8.0g birth year

age byte %8.0g age incurrent year

race byte %8.0g racelbl race

msp byte %8.0g 1 ifmarried, spouse present

nev_mar byte %8.0g 1 ifnever married

grade byte %8.0g current grade completed

collgrad byte %8.0g 1 ifcollege graduate

not_smsa byte %8.0g 1 ifnot SMSA

c_city byte %8.0g 1 ifcentral city

south byte %8.0g 1 ifsouth

ind_code byte %8.0g industry of employment

occ_code byte %8.0g occupation

union byte %8.0g 1 ifunion

wks_ue byte %8.0g weeks unemployed last year

ttl_exp float%9.0g total work experience

tenure float%9.0g job tenure, inyears

hours int %8.0g usual hours worked

wks_work int %8.0g weeks worked last year

ln_wage float%9.0g ln(wage/GNP deflator)

wage double %10.0g real wage

black float%9.0g race black

smsa byte %8.0g 1 ifSMSA

--------------------------------------------------------------------------------------

Sorted by: idcode year

下面使用Obtain full ML estimates****ML估计格式****

其中wage为工资，age grade smsa black tenure这些分别为自变量，而south black tenure为影响union的变量，union为处理变量

. etregress wage age grade smsa black tenure, treat(union = south black tenure)

Iteration 0: loglikelihood = -3140.811

Iteration 1: loglikelihood = -3053.6629

Iteration 2: loglikelihood = -3051.5847

Iteration 3: loglikelihood = -3051.575

Iteration 4: loglikelihood = -3051.575

Linear regression with endogenous treatment Number of obs = 1,210

Estimator: maximum likelihood Wald chi2(6) = 681.89

Log likelihood = -3051.575 Prob > chi2 = 0.0000

------------------------------------------------------------------------------

| Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

wage |

age | .1487409 .0193291 7.70 0.000 .1108566 .1866252

grade | .4205658 .0293577 14.33 0.000 .3630258 .4781058

smsa | .9117044 .1249041 7.30 0.000 .6668969 1.156512

black | -.7882471 .1367078 -5.77 0.000 -1.05619 -.5203048

tenure | .1524015 .0369596 4.12 0.000 .0799621 .2248409

1.union | 2.945815 .2749621 10.71 0.000 2.4069 3.484731

_cons | -4.351572 .5283952 -8.24 0.000 -5.387208 -3.315936

-------------+----------------------------------------------------------------

union |

south | -.5807419 .0851111 -6.82 0.000 -.7475566 -.4139271

black | .4557499 .0958042 4.76 0.000 .2679771 .6435226

tenure | .0871536 .0232483 3.75 0.000 .0415878 .1327195

_cons | -.8855758 .0724506 -12.22 0.000 -1.027576 -.7435753

-------------+----------------------------------------------------------------

/athrho | -.6544347 .0910314 -7.19 0.000 -.832853 -.4760164

/lnsigma | .7026769 .0293372 23.95 0.000 .645177 .7601767

-------------+----------------------------------------------------------------

rho | -.5746478 .060971 -.682005 -.4430476

sigma | 2.019151 .0592362 1.906325 2.138654

lambda | -1.1603 .1495097 -1.453334 -.8672668

------------------------------------------------------------------------------

LR testof indep. eqns. (rho = 0): chi2(1) = 19.84 Prob > chi2 = 0.0000

.

Obtain two-step consistent estimates 2阶段一致估计

etregress wage age grade smsa black tenure, treat(union = south black tenure) twostep

. etregress wage age grade smsa black tenure, treat(union = south black tenure) twoste

> p

Linear regression with endogenous treatment Number of obs = 1210

Estimator: two-step Wald chi2(8) = 566.56

Prob > chi2 = 0.0000

------------------------------------------------------------------------------

| Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

wage |

age | .1543231 .0194903 7.92 0.000 .1161227 .1925234

grade | .4225025 .029014 14.56 0.000 .3656362 .4793689

smsa | .8628628 .1285907 6.71 0.000 .6108297 1.114896

black | -.9206944 .1774617 -5.19 0.000 -1.268513 -.572876

tenure | .1003226 .051879 1.93 0.053 -.0013584 .2020037

union | 4.563859 1.006459 4.53 0.000 2.591236 6.536483

_cons | -4.670352 .5401517 -8.65 0.000 -5.72903 -3.611674

-------------+----------------------------------------------------------------

union |

south | -.4895032 .0933276 -5.24 0.000 -.6724221 -.3065844

black | .4397974 .0972261 4.52 0.000 .2492377 .6303572

tenure | .0997638 .0236575 4.22 0.000 .053396 .1461317

_cons | -.9679795 .0746464 -12.97 0.000 -1.114284 -.8216753

-------------+----------------------------------------------------------------

hazard |

lambda | -2.093313 .5801968 -3.61 0.000 -3.230478 -.9561486

-------------+----------------------------------------------------------------

rho | -0.89172

sigma | 2.3475104

------------------------------------------------------------------------------

调用数据drugexp use drugexp

Obtain control-function estimates forpotential-outcome model**控制函数估计格式**

etregress lndrug chron age lninc, treat(ins=age married lninc work) poutcomes cfunction

. webuse drugexp

(Presciption drug expenditures)

. etregress lndrug chron age lninc, treat(ins=age married lninc work) poutcomes cfunct

> ion

Iteration 0: GMM criterion Q(b) = 2.279e-15

Iteration 1: GMM criterion Q(b) = 4.501e-30

Linear regression with endogenous treatment Number of obs = 6,000

Estimator: control-function

------------------------------------------------------------------------------

| Robust

| Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

lndrug |

chron | .4671725 .0319731 14.61 0.000 .4045064 .5298387

age | .1021359 .00292 34.98 0.000 .0964128 .1078589

lninc | .0550672 .0225036 2.45 0.014 .0109609 .0991735

1.ins | -.8598836 .3483648 -2.47 0.014 -1.542666 -.1771011

_cons | 1.665539 .2527527 6.59 0.000 1.170153 2.160925

-------------+----------------------------------------------------------------

ins |

age | .021142 .0022961 9.21 0.000 .0166416 .0256424

married | .084631 .0359713 2.35 0.019 .0141286 .1551334

lninc | .1023032 .0225009 4.55 0.000 .0582022 .1464041

work | .288418 .0372281 7.75 0.000 .2154522 .3613837

_cons | -.622993 .108795 -5.73 0.000 -.8362273 -.4097587

-------------+----------------------------------------------------------------

/athrho0 | .4035094 .1724539 2.34 0.019 .0655059 .7415129

/lnsigma0 | .3159269 .0500476 6.31 0.000 .2178353 .4140184

/athrho1 | .7929459 .2986601 2.66 0.008 .2075829 1.378309

/lnsigma1 | .1865347 .0613124 3.04 0.002 .0663646 .3067048

-------------+----------------------------------------------------------------

rho0 | .3829477 .1471637 .0654124 .6300583

sigma0 | 1.37153 .0686418 1.243382 1.512885

lambda0 | .5252243 .226367 .0815532 .9688954

rho1 | .6600746 .1685343 .2046518 .880572

sigma1 | 1.205066 .0738855 1.068616 1.35894

lambda1 | .7954338 .2513036 .3028878 1.28798

------------------------------------------------------------------------------

Wald testof indep. (rho0 = rho1 = 0): chi2(2) = 8.88 Prob > chi2 = 0.0118