Predictive Analytics for Economists: Logit and Probit Models Overview

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Dive into the world of predictive analytics for economists with a focus on Logit and Probit models. Explore Maximum Likelihood Estimation, Cumulative Distributions, interpreting coefficients, and more. Enhance your understanding of binary flag variables, probability calculations, and the impact of input variable changes. Access additional resources for further learning on the Logit and Probit methods.

  • Predictive Analytics
  • Economists
  • Logit Probit Models
  • Maximum Likelihood Estimation
  • Probability
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  1. Eco 6380 Predictive Analytics For Economists Spring 2016 Professor Tom Fomby Department of Economics SMU

  2. Presentation 14 Logit and Probit Classifiers Chapter 10 in SPB

  3. Maximum Likelihood Estimation of Logit and Probit Models Binary Flag Variable 1 with probabilit y P - i P = y i 0 with probabilit y 1 i Likelihood function

  4. Cumulative Distributions for Logit and Probit Models Logit Cumulative Distribution Function Probit Cumulative Distribution Function ? ? ? = ? ? ?? ? = ?0+ ?1?1+ + ???? ? = ? ? = probability of each case

  5. Interpretation of Coefficients in Logit Model ? log Assume we change input variable ??by one unit while keeping all of the other input variables the same. Then the coefficient ??can be interpreted as the percentage change (decimal equivalent form) in the odds arising from a one unit change in ??. The signs of the logit coefficients tell us whether by increasing an input by one unit there is an increase in the log-odds (+ coefficient) or a decrease in the log-odds (- coefficient). At the same time, the signs of the logit coefficients tell you the direction of effect on the probability of a positive response with a change in an input variable. = log ???? ?? 1 ?????? 0 = ? 1 ?

  6. For more information on the Logit and Probit Methods see the pdf file Logit and Probit Notes.pdf on the class website

  7. Classroom Exercise: Exercise 9

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