# EC220 - Introduction to econometrics (chapter 10)

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EC220 - Introduction to econometrics (chapter 10)
Christopher Dougherty EC220 - Introduction to econometrics (chapter 10) Slideshow: binary choice probit models Original citation: Dougherty, C. (2012) EC220 - Introduction to econometrics (chapter 10). [Teaching Resource] © 2012 The Author This version available at: Available in LSE Learning Resources Online: May 2012 This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License. This license allows the user to remix, tweak, and build upon the work even for commercial purposes, as long as the user credits the author and licenses their new creations under the identical terms.

BINARY CHOICE MODELS: PROBIT ANALYSIS
In the case of probit analysis, the sigmoid function is the cumulative standardized normal distribution. 1

BINARY CHOICE MODELS: PROBIT ANALYSIS
The maximum likelihood principle is again used to obtain estimates of the parameters. 2

BINARY CHOICE MODELS: PROBIT ANALYSIS
. probit GRAD ASVABC SM SF MALE Iteration 0: log likelihood = Iteration 1: log likelihood = Iteration 2: log likelihood = Iteration 3: log likelihood = Iteration 4: log likelihood = Probit estimates Number of obs = LR chi2(4) = Prob > chi2 = Log likelihood = Pseudo R = GRAD | Coef. Std. Err z P>|z| [95% Conf. Interval] ASVABC | SM | SF | MALE | _cons | Here is the result of the probit regression using the example of graduating from high school. 3

BINARY CHOICE MODELS: PROBIT ANALYSIS
. probit GRAD ASVABC SM SF MALE Iteration 0: log likelihood = Iteration 1: log likelihood = Iteration 2: log likelihood = Iteration 3: log likelihood = Iteration 4: log likelihood = Probit estimates Number of obs = LR chi2(4) = Prob > chi2 = Log likelihood = Pseudo R = GRAD | Coef. Std. Err z P>|z| [95% Conf. Interval] ASVABC | SM | SF | MALE | _cons | As with logit analysis, the coefficients have no direct interpretation. However, we can use them to quantify the marginal effects of the explanatory variables on the probability of graduating from high school. 4

BINARY CHOICE MODELS: PROBIT ANALYSIS
As with logit analysis, the marginal effect of Xi on p can be written as the product of the marginal effect of Z on p and the marginal effect of Xi on Z. 5

BINARY CHOICE MODELS: PROBIT ANALYSIS
The marginal effect of Z on p is given by the standardized normal distribution. The marginal effect of Xi on Z is given by bi. 6

BINARY CHOICE MODELS: PROBIT ANALYSIS
As with logit analysis, the marginal effects vary with Z. A common procedure is to evaluate them for the value of Z given by the sample means of the explanatory variables. 7

BINARY CHOICE MODELS: PROBIT ANALYSIS
. sum GRAD ASVABC SM SF MALE Variable | Obs Mean Std. Dev Min Max GRAD | ASVABC | SM | SF | MALE | As with logit analysis, the marginal effects vary with Z. A common procedure is to evaluate them for the value of Z given by the sample means of the explanatory variables. 8

BINARY CHOICE MODELS: PROBIT ANALYSIS
Probit: Marginal Effects mean b product f(Z) f(Z)b ASVABC SM –0.008 – –0.001 SF MALE constant 1.00 –1.451 –1.451 Total In this case Z is equal to when the X variables are equal to their sample means. 9

BINARY CHOICE MODELS: PROBIT ANALYSIS
Probit: Marginal Effects mean b product f(Z) f(Z)b ASVABC SM –0.008 – –0.001 SF MALE constant 1.00 –1.451 –1.451 Total We then calculate f(Z). 10

BINARY CHOICE MODELS: PROBIT ANALYSIS
Probit: Marginal Effects mean b product f(Z) f(Z)b ASVABC SM –0.008 – –0.001 SF MALE constant 1.00 –1.451 –1.451 Total The estimated marginal effects are f(Z) multiplied by the respective coefficients. We see that a one-point increase in ASVABC increases the probability of graduating from high school by 0.4 percent. 11

BINARY CHOICE MODELS: PROBIT ANALYSIS
Probit: Marginal Effects mean b product f(Z) f(Z)b ASVABC SM –0.008 – –0.001 SF MALE constant 1.00 –1.451 –1.451 Total Every extra year of schooling of the mother decreases the probability of graduating by 0.1 percent. Father's schooling has no discernible effect. Males have 0.4 percent higher probability than females. 12

BINARY CHOICE MODELS: PROBIT ANALYSIS
Logit Probit Linear f(Z)b f(Z)b b ASVABC SM –0.001 –0.001 –0.002 SF MALE –0.007 The logit and probit results are displayed for comparison. The coefficients in the regressions are very different because different mathematical functions are being fitted. 13

BINARY CHOICE MODELS: PROBIT ANALYSIS
Logit Probit Linear f(Z)b f(Z)b b ASVABC SM –0.001 –0.001 –0.002 SF MALE –0.007 Nevertheless the estimates of the marginal effects are usually similar. 14

BINARY CHOICE MODELS: PROBIT ANALYSIS
Logit Probit Linear f(Z)b f(Z)b b ASVABC SM –0.001 –0.001 –0.002 SF MALE –0.007 However, if the outcomes in the sample are divided between a large majority and a small minority, they can differ. 15

BINARY CHOICE MODELS: PROBIT ANALYSIS
Logit Probit Linear f(Z)b f(Z)b b ASVABC SM –0.001 –0.001 –0.002 SF MALE –0.007 This is because the observations are then concentrated in a tail of the distribution. Although the logit and probit functions share the same sigmoid outline, their tails are somewhat different. 16

BINARY CHOICE MODELS: PROBIT ANALYSIS
Logit Probit Linear f(Z)b f(Z)b b ASVABC SM –0.001 –0.001 –0.002 SF MALE –0.007 This is the case here, but even so the estimates are identical to three decimal places. According to a leading authority, Amemiya, there are no compelling grounds for preferring logit to probit or vice versa. 17

BINARY CHOICE MODELS: PROBIT ANALYSIS
Logit Probit Linear f(Z)b f(Z)b b ASVABC SM –0.001 –0.001 –0.002 SF MALE –0.007 Finally, for comparison, the estimates for the corresponding regression using the linear probability model are displayed. 18

BINARY CHOICE MODELS: PROBIT ANALYSIS
Logit Probit Linear f(Z)b f(Z)b b ASVABC SM –0.001 –0.001 –0.002 SF MALE –0.007 If the outcomes are evenly divided, the LPM coefficients are usually similar to those for logit and probit. However, when one outcome dominates, as in this case, they are not very good approximations. 19