離散選擇模型(編輯修改稿)
2024-10-22 19:01 本頁面
【文章內(nèi)容簡介】 .07581 .641 high*| .1470987 .0247 .098687 .195511 .568 ? ? (*) dy/dx is for discrete change of dummy variable from 0 to 1 marginal effects coefficient estimation using dprobit (和前一頁結(jié)果一樣 ) ? union | dF/dx Std. Err. z P|z| xbar [ 95% . ] ? + potexp|.0226964 .0041529 .014557 .030836 exp2| .000085 grade| .0051379 married*|. .641 .07581 high*|.1470987 .0247005 .568 .098687 .195511 ? + ? obs. P | .216 ? pred. P | .1904762 (at xbar) 默認的 。當然可以指定其他值 。 ? ? (*) dF/dx is for discrete change of dummy variable from 0 to 1 ? z and P|z| are the test of the underlying coefficient being 0 Marginal index effcts vs marginal probability effects evaluated at sample mean ? 邊際指數(shù)效應 :就是對應的估計系數(shù)值 。 ? 在樣本均值處的邊際概率效應等于相應的估計系數(shù)值乘以相應的在樣本均值處的概率密度函數(shù)值 . 以教育為例的結(jié)果 ? scalar pdfxallbar=normden(e(xbar)) (注依次是 dprobit 之后沒有 at(.)。如果有 at(.),則可以寫為 : scalar pdfxallbar=normden(e(at)) . ? lin _b[grade]*pdfxallbar (在 dprobit之后使用 ) ? ( 1) .2717829 grade = 0 union|Coef. Std. Err. z P|z| [95% Conf. Interval] ? + (1)| .00513 ? ? 藍色的是概率偏效應系數(shù) 。綠色是指數(shù)偏效應 (probit)系數(shù) 。 ? 具體細節(jié)見程序說明 。 t 檢驗和 F檢驗 ? test potexp=grade ? ( 1) potexp grade = 0 ? chi2( 1) = 。 Prob chi2 = ? . test potexp grade exp2 married high ? ( 1) potexp = 0 ? ( 2) grade = 0 ? ( 3) exp2 = 0 ? ( 4) married = 0 ? ( 5) high = 0 ? chi2( 5) = 。Prob chi2 = Ordered probit model ? oprobit depvar [varlist][weight][if exp][in range][,table robust cluster(varname) score(newvarlist) level() maximize_options] ? predict [type] newvarname [if exp][in range][,{p|xb|stdp}oute(oute) nooffset] ? Options:k:number of categories ? table showing how probability are puted from the fitted equation. ? cluster : specifies observations are independent across groups(clusters) but not necessary within groups。it equal to robust。 ? score:first is dLnLj/dln(xjb)。second is dLnLj/d_cut1j ? Kth is dLnLj/d_cut(k1)j Options for prediction ? p:default calculate predicted probabilities. If don’t specify oute(),you must specify k new variables,k is number of categories of dependent variables。if you specify oute(),you must specify one new variable。 ? xb,stdp,nooffset as the same as probit option ? oute(oute) :specifies for which oute the predicted probabilities to be puted,it should contain either one single value of dependent variable,or one of 1,2,… ? 1 represent the first category of dependent variable. 例子：對自然資源愿意支付的程度 ? 每個人都面臨著三個標的：初始的，以及上標和下標； 是觀測不到的個人支付意愿。因變量 depvar。自變量： Age,ine 均為分組變量； *iB?????????????????)Ba c c e p t e d ( Bg e t b i d sb o t h if 4)BBr e j e c t e d ( B g e t s s e c o n d a n d a c c e p t e dg e t b i d sf i r st if 3)BB(B a c c e p t e dg e t s e c o n d a n dr e j e c t e d ,g e t b i d sf i r st t h eif 2)。Br e j e c t e d ( Bg e t b i d sb o t h if 1Ui*iUi*iIiIi*iLiLi*i*iiiiyxB ??輸出結(jié)果 ? Ordered probit estimates LR chi2(3) = ? Prob chi2 = ? Log likelihood = Pseudo R2 = ? ? depvar| Coef. Std. Err. z P|z| [95% Conf. Interval] ? + ? age | .0437968 female | .128588 .0083811 ine | .1507535 .0507301 .0513242 .2501827 ? depvar | Probability Observed ? + ?
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