Using Stata to Estimate an Ordered Logit Model of Chinese Fertility The dependent variable is called CEB3, an ordinal variable scored 1 if the woman has no births, 2 if the woman has few(1-2)births, and 3 if the woman has multiple(3+)births. Thus, the outcomes of tab c college|-1,27586265286和自0n-1,7%821,7591新 variable are three none.few-/、m.mm cebu 1 Probability coerver bouc Total I 4. 134 Ordinal logistic regression is used to lote that the seven logit coefficients have model the CEB3 dependent variable; the single values(which is not like the X variables are AGE(in years), and six situation in last lecture when estimate a dummy variables representing place of multinomial logistic regression) residence, ethnicity and education Note also the two cut points of cut1=0.92, URBAN, HAN, PRIMARY, JUNIOR, nd cut= 6.53 these are the so-called SENIOR COLLEGE ancillary parameters. Their values assist The Stata command is logit, following by s in calculating probabilities for each the dependent variable followed by the woman of her being in each of the three independent variables outcomes on the cEB3 dependent riable; they also assist in interpreting the logit coefficients and their odds ratios13 25 • The dependent variable is called CEB3, an ordinal variable scored 1 if the woman has no births, 2 if the woman has few (1-2) births, and 3 if the woman has multiple (3+) births. Thus, the outcomes of the outcomes of the dependent the dependent variable are variable are three: none, few, three: none, few, multiple. multiple. Using Stata to Estimate an Ordered Logit Model of Chinese Fertility 26 • Ordinal logistic regression is used to model the CEB3 dependent variable; the X variables are AGE (in years), and six dummy variables representing place of residence, ethnicity and education: URBAN, HAN, PRIMARY, JUNIOR, SENIOR, COLLEGE. • The Stata command is ologit, following by the dependent variable followed by the independent variables. 14 27 28 • Note that the seven logit coefficients have single values (which is not like the situation in last lecture when I estimate a multinomial logistic regression). • Note also the two cut points of cut1 = 0.92, and cut2 = 6.53; these are the so-called ancillary parameters. Their values assist us in calculating probabilities for each woman of her being in each of the three outcomes on the CEB3 dependent variable; they also assist in interpreting the logit coefficients and their odds ratios