2. We care about proportion for categorical variable, but average for numerical one.

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1 Probit Model 1. We apply Probit model to Bank data. The dependent variable is deny, a dummy variable equaling one if a mortgage application is denied, and equaling zero if accepted. The key regressor is debt-income ratio, diratio. 2. We care about proportion for categorical variable, but average for numerical one.. import excel "I:\420\420_bank.xls", sheet("sheet1") firstrow clear. label define denyl 0 "Accept" 1 "Deny". label value deny denyl. tab deny deny Freq. Percent Cum Accept 2, Deny Total 2, sum diratio Variable Obs Mean Std. Dev. Min Max diratio So about 12 percent applications are denied. The average diratio is We can use deny as independent variable. By doing so, the following regression effectively reports the two-sample t test for diratio:. reg diratio deny, nohe diratio Coef. Std. Err. t P> t [95% Conf. Interval] deny _cons

2 The average diratio is for the acceptance group (base or reference group, when deny = 0). The average diratio for the denial group is greater than the acceptance group by This positive difference is statistically significant with t-value = This regression shows that diratio is correlated with the probability of acceptance. 4. Now we switch, and use deny as the dependent variable:. reg deny diratio, r Robust deny Coef. Std. Err. t P> t [95% Conf. Interval] diratio _cons This regression reports the Linear Probability Model (LPM) with heteroskedasticityrobust standard error. y i = β 0 + β 1 x i + u i (1) E(y i x i ) = β 0 + β 1 x i (2) P r(y i = 1 x i ) = β 0 + β 1 x i (3) where the second step assumes E(u i x i ) = 0, and the last step is due to the fact that for a Bernoulli variable E(y) = P r(y = 1). It is called LPM because it assumes the probability is a linear function of x, see (3). 5. Exercise: why is the robust standard error necessary here? 6. So, suppose the diratio changes from 0.1 to 0.2, the probability for denial increases by = β = Actually the change in denial probability is the same as when diratio changes from 0.2 to 0.3, from 0.3 to 0.4, and so on. In short, dp r(y=1) LPM assumes that the marginal effect of x on P r(y = 1) is constant: = β dx The constant marginal effect implies that the predicted probability P r(y = 1) can be greater than 1 if x is sufficiently large. This is bad because probability should be bounded between 0 and 1. For instance, below we find a greater-than-one probability: 2

3 . dis "predicted denial probability when diratio=2 is " _b[_cons] + _b[diratio]* predicted denial probability when diratio=2 is twoway (scatter deny diratio) (lfit deny diratio), ytitle(deny) Deny diratio deny Fitted values The graph above clearly shows that LPM may also produce negative P r(y = 1) when x is sufficiently small. 8. In short, LPM is flawed because it does not impose the 0-1 boundary on P r(y i = 1 x i ). By comparison, probit and logit models impose that restriction by letting P r(y i = 1 x i ) = cdf(β 0 + β 1 x i ) (4) where cdf(z) = Pr(Z z) denotes the nonlinear cumulative distribution function (cdf), and by definition 0 cdf 1. Probit model uses the cdf of standard normal distribution, whereas Logit model uses the cdf of logistic distribution. 9. Consider Probit first. Let Φ( ) be the cdf for standard normal distribution. Then the density for the i-th observation is f i = p y i i (1 p i) 1 y i, (y i = 0, 1) (5) p i = Φ(β 0 + β 1 x i ) (6) Assuming iid sample, the joint density (likelihood) is L = Π n i=1f i (7) 3

4 After taking log, we obtain the log likelihood as log(l) = i log(f i ) = i [y i log(p i ) + (1 y i ) log(1 p i )] (8) 10. Notice that p i is no longer a constant. Instead, it is the conditional probability P r(y i = 1 x i ), which varies with x i. Because of this complication, it is impossible to find a closed-form solution or analytical answer for maximizing log(l). Nevertheless, numerical method can be used to solve the optimization problem though iterations:. probit de di Iteration 0: log likelihood = Iteration 1: log likelihood = Iteration 2: log likelihood = Iteration 3: log likelihood = Probit regression Number of obs = 2380 LR chi2(1) = Prob > chi2 = Log likelihood = Pseudo R2 = deny Coef. Std. Err. z P> z [95% Conf. Interval] diratio _cons In the end of iteration, the log likelihood is maximized at We may consider the restricted regression in which diratio is excluded. qui probit de. dis "log likelihood for restricted regression is " e(ll) log likelihood for restricted regression is dis "Likelihood Ratio (LR) test is " 2*( ( )) Likelihood Ratio (LR) test is dis "pvalue of LR test is " chi2tail(1, 80.59) pvalue of LR test is 2.778e-19 4

5 So the LR test rejects the null hypothesis that diratio does not matter for deny. You can think of LR test as the maximum likelihood version of F test. The Pseudo R2 is computed as. dis "pseudo R2 is " 1 - ( )/( ) pseudo R2 is According to (6), for a given value of diratio, say 0.3, the denial probability is. dis "denial probability when diratio is 0.3 is " normprob( *0.3) denial probability when diratio is 0.3 is Actually, we can apply a loop to obtain denial probability for a range of diratio forvalues i = 1 (1) 9 { local di = i *0.1 dis "denial probability when diratio is " di " is " normprob( * di ) } denial probability when diratio is.1 is denial probability when diratio is.2 is denial probability when diratio is.3 is denial probability when diratio is.4 is denial probability when diratio is.5 is denial probability when diratio is.6 is denial probability when diratio is.7 is denial probability when diratio is.8 is denial probability when diratio is.9 is Notice that the change in denial probability is NOT constant. Good News! 12. Non-constant marginal effect can be translated to the 0-1 boundary on predicted probability. capture drop dis pd gen dis =. gen pde =. 5

6 forvalues i = 1 (1) 20 { local di = i *0.1 local pd = normprob( * di ) qui replace dis = di in i qui replace pde = pd in i } label variable dis "debt-income ratio" label variable pd "predicted denail probability" twoway (connect pde dis) in 1/20 predicted denail probability debt income ratio We see the predicted denial probability is restricted to be between 0 and 1. Good news! From that graph we know denial is almost sure when diratio exceeds The downside of the probit model is that β 1 becomes hard to interpret. By taking derivative of (6) and applying chain rule dp r(y i = 1 x i ) dx i = dφ(β 0 + β 1 x i ) dx i = ϕ(β 0 + β 1 x i )β 1 (9) where ϕ is the probability density function (pdf) of standard normal distribution, the derivative of Φ. In short, β 1 multiplied by a factor ϕ(β 0 + β 1 x i ) gives the marginal effect; β 1 alone does not. 14. Exercise: (True or False) the sign of marginal effect dp r(y i=1 x i ) dx i sign of β 1. only depends on the 6

7 15. Equation [17.14] of the textbook suggests replacing x i with its sample average. qui sum diratio. sca factor1 = normalden( *r(mean)). dis "marginal effect of diratio on denial probability is " 2.97*factor1 marginal effect of diratio on denial probability is Alternatively, we can use equation [17.17]:. gen gxb = normalden( *diratio). qui sum gxb. dis "marginal effect of diratio on denial probability is " *r(mean) marginal effect of diratio on denial probability is The new version stata offers a command called margins to obtain the marginal effect directly using [17.17]. qui probit den dira. margins, dydx(dira) Average marginal effects Number of obs = 2380 Expression : Pr(deny), predict() dy/dx w.r.t. : diratio Delta-method dy/dx Std. Err. z P> z [95% Conf. Interval] diratio So on average, the denial probability rises by =( )(0.1) when diratio increases by 0.1. The effect of diratio on denial probability is significant with z-value (Optional) Finally, there is a set of stata commands that allow you to specify the log likelihood explicitly and do the MLE 7

8 capture program drop lfprobit program lfprobit version 10.0 args lnf xb local y "$ML_y1" quietly replace lnf = ln( normal( xb )) if y ==1 quietly replace lnf = ln(1-normal( xb )) if y ==0 end ml model lf lfprobit (deny = diratio) ml maximize initial: log likelihood = alternative: log likelihood = rescale: log likelihood = Iteration 0: log likelihood = Iteration 1: log likelihood = Iteration 2: log likelihood = Iteration 3: log likelihood = Iteration 4: log likelihood = Number of obs = 2380 Wald chi2(1) = Log likelihood = Prob > chi2 = deny Coef. Std. Err. z P> z [95% Conf. Interval] diratio _cons (Optional) We can drive the equation (6) by using a latent variable, say badness (score), which is a linear function of debt-income ratio. We assume a mortgage application is 8

9 denied if badness is positive P r(y i = 1) = P r(badness > 0) = P r(β 0 + β 1 x i + u i > 0) = P r(u > β 0 β 1 x i ) = 1 Φ( β 0 β 1 x i ) = Φ(β 0 + β 1 x i ) where the second to last step assumes that u i follows standard normal distribution. 9

10 Logit Model and Odds Ratio 1. The only difference is, Logit model (or Logistic regression) replaces (6) with where p i = e z 1+e z is the cdf of the logistic distribution. 2. Exercise: verify that the cdf is between 0 and 1. eβ 0+β 1 x i 1 + e β 0+β 1 x i (10) 3. The intercept coefficient and slope coefficient of logit model are NOT comparable to those of probit model.. logit deny diratio Iteration 0: log likelihood = Iteration 1: log likelihood = Iteration 2: log likelihood = Iteration 3: log likelihood = Iteration 4: log likelihood = Logistic regression Number of obs = 2380 LR chi2(1) = Prob > chi2 = Log likelihood = Pseudo R2 = deny Coef. Std. Err. z P> z [95% Conf. Interval] diratio _cons However, the marginal effect, which is what really matters, is similar:. margin, dydx(diratio) Average marginal effects Number of obs = 2380 Expression : Pr(deny), predict() dy/dx w.r.t. : diratio 10

11 Delta-method dy/dx Std. Err. z P> z [95% Conf. Interval] diratio So on average, the denial probability rises by =( )(0.1) when diratio increases by 0.1. Notice that This average marginal effect is also close to that reported by LPM ( ), which assumes the marginal effect is constant. 5. (Optional) The set of commands to do MLE explicitly for logit model is capture program drop lflogit program lflogit end version 10.0 args lnf xb local y "$ML_y1" quietly replace lnf = ln( invlogit( xb )) if y ==1 quietly replace lnf = ln(1-invlogit( xb )) if y ==0 ml model lf lflogit (deny = diratio) ml maximize 6. Logit model is very popular in industry due to the fact that β 1 and e β 1 have intuitive interpretations. Notice that (drop β 0 for simplicity) where p i 1 p i log p i = 1 p i = p i e β 1x i 1 + e β 1x i (11) e β 1x i (12) = e β 1x i (13) 1 p ) i = β 1 x i (14) 1 p i ( pi is called odds for denial, and β 1 measures the effect of x on log of odds. 11

12 7. It is especially interesting if x is a dummy variable as well. For instance, we have a dummy variable called black:. logit deny black, nolog Logistic regression Number of obs = 2380 LR chi2(1) = Prob > chi2 = Log likelihood = Pseudo R2 = deny Coef. Std. Err. z P> z [95% Conf. Interval] black _cons So the log of denial odds for black is greater than white by According to (13), the denial odds for (w)hite and (b)lack are p w i 1 p w i p b i 1 p b i = e β 1(0) = e β 1(1) (15) (16) p b i 1 p b i p w i 1 p w i = e β 1 (17) Equation (17) indicates that the odds ratio is given by e β 1.. dis "odds ratio is " exp( ) odds ratio is logit deny black, or nolog deny Odds Ratio Std. Err. z P> z [95% Conf. Interval] black _cons

13 So the option or can report the odds ratio directly. 9. (Optional) This is the idea of Delta method. Apply the Middle Value Theorem to a nonlinear function g( ˆβ) : g( ˆβ) = g(β) + g ( β)( ˆβ β), where β is between β and ˆβ. It follows that var(g( ˆβ)) [g ( ˆβ)] 2 var( ˆβ) sd(g( ˆβ)) g ( ˆβ) sd( ˆβ) For this problem, g( ˆβ 1 ) = e ˆβ 1, and g ( ˆβ 1 ) = e ˆβ 1.. dis "standard error of exp(beta1) is " exp( )* standard error of exp(beta1) is Alternatively, the odds ratio can be obtained from two-way table. label define blackl 0 "white" 1 "black". label value black blackl. tab black deny, row Key frequency row percentage deny black Accept Deny Total white 1, , black

14 Total 2, , So the denial odds for white is = ; the denial odds for black is = ; finally, the odds ratio is = , the same number reported by command logit deny black, or 11. The advantage of logit model over the two-way table is that the former can control for other factors. logit deny black diratio, or nolog Logistic regression Number of obs = 2380 LR chi2(2) = Prob > chi2 = Log likelihood = Pseudo R2 = deny Odds Ratio Std. Err. z P> z [95% Conf. Interval] black diratio _cons dis "t test of odds ratio = 1 is " ( )/ t test of odds ratio = 1 is So after holding constant debt-income ratio, black s denial odds is more than three times of white. With t-value = we can easily reject the null hypothesis of odds ratio = 1, that is, race does not matter for denial probability. 14

15 Multinomial Logit Model (Optional) 1. In this case, the dependent variable is still categorical, however, there are more than two outcomes. In terms of statistics, the random variable follows multinomial distribution. 2. We use the data provided by stata to illustrate multinomial logit model. webuse sysdsn3 (Health insurance data). list insure age in 1/ insure age Indemnity Prepaid Uninsure Prepaid tab insure insure Freq. Percent Cum Indemnity Prepaid Uninsure Total The dependent variable is insure (insurance plan), a categorical variable that can take three values: Indemnity, Prepaid and Uninsure. We want to know how age affects people s decision about insurance plan. 3. One option is to run two logit models seperatedly, one compares prepaid to indemnity, and the other compares uninsure to indemnity. Toward that end, we generate three dummy varables, one for each category:. qui tab insure, gen(y) 15

16 . tab y2 insure==pre paid Freq. Percent Cum Total tab y3 insure==uni nsure Freq. Percent Cum Total Then we run two seperate logistic regressions:. logit y2 age if y3==0, nolog y2 Coef. Std. Err. z P> z [95% Conf. Interval] age _cons logit y3 age if y2==0, nolog y3 Coef. Std. Err. z P> z [95% Conf. Interval] age _cons So when a person gets one year older, the log of odds (prepaid vs indemnity) goes downs 16

17 by ; the log of odds (uninsure vs indemnity) goes downs by Both changes are insignificant. You can add option or to get the odds-ratio interpretation. 4. Option two is using command mlogit, which conducts a joint comparison:. mlogit insure age, nolog Multinomial logistic regression Number of obs = 615 LR chi2(2) = 1.96 Prob > chi2 = Log likelihood = Pseudo R2 = insure Coef. Std. Err. z P> z [95% Conf. Interval] Indemnity (base outcome) Prepaid age _cons Uninsure age _cons We see the coefficient of age in this multinomial logistic regression is similar to the group-wise logistic regression. 5. Let x denote the outcome-invariant regressor, e.g., age. Suppose outcome one is the base outcome. Let β 1 be the coefficient of x when outcome two is compared to outcome one; β 2 be the coefficient of x when outcome three is compared to outcome one. The 17

18 multinomial logit model assumes that P r(y = outcome one) = P r(y = outcome two) = P r(y = outcome three) = 1 (18) 1 + e β 1x + e β 2x e β 1x 1 + e β 1x + e β 2x (19) e β 2x 1 + e β 1x + e β 2x (20) Equivalently, e β 1x e β 2x = = P r(y = outcome two) P r(y = outcome one) P r(y = outcome three) P r(y = outcome one) (21) (22) This model imposes a restriction called Independence of Irrelevant Alternatives (IIA). That is, the comparison of outcomes one and two has nothing to do with outcome three since β 2 is absent in (21). IIA explains why we get very similar results from mlogit and two separated logit. 6. We can use a loop to figure out probability for each outcome for a range of age local ag = 20 while ag <=80 { local xb1 = ( )* ag local xb2 = ( )* ag dis "age is " ag dis "probability of indemnity is " 1/(1+exp( xb1 )+exp( xb2 )) dis "probability of prepaid is " exp( xb1 )/(1+exp( xb1 )+exp( xb2 )) dis "probability of uninsured is " exp( xb2 )/(1+exp( xb1 )+exp( xb2 )) dis "" local ag = ag + 10 } age is 20 probability of indemnity is probability of prepaid is probability of uninsured is

19 ... age is 80 probability of indemnity is probability of prepaid is probability of uninsured is The ML commands are qui tab insure, gen(pr) global y1 "pr1" global y2 "pr2" global y3 "pr3" capture program drop lfmlogit program lfmlogit version 10.0 args lnf xb1 xb2 tempvar p1 p2 p3 quietly { gen double p1 = 1/(1+exp( xb1 )+exp( xb2 )) gen double p2 = exp( xb1 )/(1+exp( xb1 )+exp( xb2 )) gen double p3 = exp( xb2 )/(1+exp( xb1 )+exp( xb2 )) replace lnf = $y1*ln( p1 ) + $y2*ln( p2 ) + $y3*ln( p3 ) } end ml model lf lfmlogit (eq1: insure = age) (eq2: insure = age) ml maximize 19

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