計(jì)量經(jīng)濟(jì)學(xué)-實(shí)驗(yàn)三

1、精選優(yōu)質(zhì)文檔-傾情為你奉上浙 江 財(cái) 經(jīng) 大 學(xué)實(shí) 驗(yàn)（實(shí)訓(xùn)）報(bào) 告項(xiàng) 目 名 稱(chēng) logistic or probit model 所屬課程名稱(chēng) 計(jì)量經(jīng)濟(jì)學(xué) 項(xiàng) 目 類(lèi) 型 驗(yàn)證性實(shí)驗(yàn) 實(shí)驗(yàn)(實(shí)訓(xùn))日期 15年04月 日 班 級(jí) 學(xué) 號(hào) 姓 名 指導(dǎo)教師 浙江財(cái)經(jīng)大學(xué)教務(wù)處制一、實(shí)驗(yàn)（實(shí)訓(xùn)）概述：【目的及要求】目的: 當(dāng)被解釋變量是虛擬變量時(shí)，學(xué)會(huì)用Logistic model或Probit model進(jìn)行估計(jì)，掌握似然比（LR）檢驗(yàn)，學(xué)會(huì)解釋模型的估計(jì)值。要求: 掌握Logistic model或Probit model的估計(jì)，按具體的題目要求完成實(shí)驗(yàn)報(bào)告,并及時(shí)上傳到給定的FTP！【基本

2、原理】MLE【實(shí)施環(huán)境】（使用的材料、設(shè)備、軟件）STATA軟件二、實(shí)驗(yàn)（實(shí)訓(xùn)）內(nèi)容：【項(xiàng)目?jī)?nèi)容】Logistic model或Probit model的估計(jì)【方案設(shè)計(jì)】題目來(lái)自 Wooldridge chapter 17 C17.8?！緦?shí)驗(yàn)（實(shí)訓(xùn)）過(guò)程】（步驟、記錄、數(shù)據(jù)、程序等）附后【結(jié)論】（結(jié)果、分析）附后三、指導(dǎo)教師評(píng)語(yǔ)及成績(jī)：評(píng)語(yǔ)：成績(jī)： 優(yōu) 指導(dǎo)教師簽名：倪偉才 批閱日期：15年04月實(shí)驗(yàn)三報(bào)告Logistic model，Probit model（驗(yàn)證性實(shí)驗(yàn)）實(shí)驗(yàn)類(lèi)型：驗(yàn)證性實(shí)驗(yàn)實(shí)驗(yàn)?zāi)康模寒?dāng)被解釋變量是虛擬變量時(shí)，學(xué)會(huì)用Logistic model或Probit model進(jìn)行估

3、計(jì)，掌握似然比（LR）檢驗(yàn)，學(xué)會(huì)解釋模型的估計(jì)值。實(shí)驗(yàn)內(nèi)容：Logistic model或Probit model的估計(jì)實(shí)驗(yàn)要求：掌握Logistic model或Probit model的估計(jì)，按具體的題目要求完成實(shí)驗(yàn)報(bào)告,并及時(shí)上傳到給定的FTP！實(shí)驗(yàn)題目：abstracted from chapter17 C17.8The file JTRAIN2.dta ontains data on a job training experimentfor a group of men. Men could enter the program starting in January 1976 up

4、through about mid-1977.The program ended in December 1977.The idea is to test whether participation in the job training program had an effect on unemployment probabilities and earnings in 1978. 就業(yè)培訓(xùn)是否對(duì)失業(yè)率及收益有影響(i)The variable train is the job training indictor. How many men in the example participat

5、ed in the job training program? What was the highest number of months a man actually participated in the program? (consider the variable mosinex).(ii)Run a linear regression of train on several demographic and pretraining variables:unem74,unem75,age,educ,black,hisp,and married. Are these variables j

6、ointly significant at the 5% level?(iii)Estimate a probit version of the linear model in part(ii).Compute the likelihood ratio test for joint significance of all variables .What do you conclude?(iv) Run a simple regression of unem78 on train and report the results in equation form. What is the estim

7、ated effect of participating in the job training program on the probability of being unemployed in 1978? Is it statistically significant?(v)Run a probit of unem78 on train .Does it make sense to compare the probit coefficient on train with the coefficient obtained from the linear model in part(v)?(v

8、i)Find the fitted probabilities from parts(v) and (vi).Explain why they are identical.Which approach would you use to measure the effect and statistical significance of the job training program?(vii)Add all of the variables from part(ii) as additional controls to the models from parts(v) and (vi).Ar

9、e the fitted probabilities now identical? What is the correlation between them? 實(shí)驗(yàn)題目分析報(bào)告：(i)sum train if train=1445人中有185人參加就業(yè)培訓(xùn)計(jì)劃sum mosinex實(shí)驗(yàn)中時(shí)間最長(zhǎng)的為24個(gè)月(ii)reg train unem74 unem75 age educ black hisp marriedF（7，437）=1.43.p=0.1915，5%的置信水平上聯(lián)合顯著(iii)probit train unem74 unem75 age educ black hisp marr

10、iedP(train = 1|x) = F(b0 + b1unem74 + b2unem75 + b3age + b4educ + b5black + b6hisp + b7married) LR chi2（7）=10.18，p=0.1785，和第二題中LPM獲得的近似。(iv) reg unem78 trainunem= 0.35 - 0.11train （0.028） （0.044）n=445，=0.0139參加在職培訓(xùn)的在1987年失業(yè)率下降了0.111，這是很大的影響，沒(méi)有參加培訓(xùn)的失業(yè)率為0.354，培訓(xùn)將失業(yè)率降低至0.243，這個(gè)差異在1%的雙側(cè)檢驗(yàn)下具有顯著的統(tǒng)計(jì)意義。(v)p

11、robit unem78 train (0.080) (0.128)與題目四模型中的系數(shù)比較無(wú)意義，但兩個(gè)模型的t統(tǒng)計(jì)量相同。(vi)qui reg unem78 trainpredict lhat(option xb assumed; fitted values)tabulate lhatqui probit unem78 trainpredict phat(option pr assumed; Pr(unem78)tabulate phat(vii) qui reg unem78 train unem74 unem75 age educ black hisp marriedpredict l2hat(option xb assumed; fitted values)qui probit unem78 train unem74 unem75 age educ bla