# multinomial logit & ordered probit. multinomial logit is used when the data cannot be ordered. an...

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Multinomial Logit & Ordered Probit Slide 2 Multinomial Logit Is used when the data cannot be ordered. An example is choice of holiday: (i) beach, (ii) mountain, (iii) culture. For each individual they are go on just one holiday. We will examine this within the context of insurance data. The exact meaning does not matter, just treat it like holiday data. But for a clue type: describe summ *ins* label list insure Slide 3 use http://www.stata-press.com/data/r11/sysdsn1.dta,clear There are 3 options: those who prepay, those who are not insured and those who are covered by an indemnity generate site1=site==1 generate site2=site==2 generate site3=site==3 NOW TYPE: mlogit insure age male nonwhite site2 site3 Slide 4 Note two equations one to exalpain those who opt for prepaid and a second for those who opt for uninsure Slide 5 But there are three choices, so why two equations. Well if you know the determinants of two of the choices the third comes about from default. It can also be viewed as the default choice against which the other two are being compared. Here the default case is the first, indemnity. Could we change it? YES. Slide 6 mlogit insure age male nonwhite site2 site3, base(2) This will change the default case to the second option. Slide 7 Slide 8 Data also comes from: use http://www.stata- press.com/data/r11/sysdsn1.dta mlogit insure age male nonwhite Slide 9 Clear, set memory and load data clear set mem 100000 use "http://staff.bath.ac.uk/hssjrh/oprob.dta" Slide 10 Describe pers Slide 11 The variable relates to a persons situation and how it has changed over the last five years. Let us look at it. Type: tab2 pers pers Slide 12 The most common response was improved, but for over half of the sample this was not the case Slide 13 Ordered probit We use this when we have discrete data and when it is ordered. In this case 1 best (improved) 2 next best (stayed about the same) 3 worst (got worse). The ordering is clear. Slide 14 Change in personal situation Assume an underlying and continuous variable relating to changes in the individuals personal situation Slide 15 Change in personal situation If this underlying variable is to the left of 1 we classify the variable as 1 the individuals position has improved Slide 16 Change in personal situation If this underlying variable is to the right of 2 we classify the variable as 3 the individuals position has got worse Slide 17 Change in personal situation In between these two values we classify the variable as 2 the individuals position has stayed the same Slide 18 You might say: surely stay the same is one specific value (perhaps 0) anything to the left of this has improved and anything to the right has got worse. But it is common to assume a range of values which denote too small a change to denote either improve or got worse and these values are 2 and 1 Slide 19 Do the estimation. Simply use oprobit rather than regress. oprobit persi lgnipc male age agesq rlaw estonia village town selfemp marrd educ2 unemp manual if age 17 & persi