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Modeling and Forecasting

  1. What is a linear probability model.
  2. Is there anything special one should do when estimating a linear probability model.
  3. What other models can we use if we have a dichotomous dependent variable.
  4. What are the advantages/disadvantages of linear probability models vis a vis the alternatives?

Computer Exercise:

Use the dataset loanapp to estimate the impact of race on the probability that a loan application is approved.

  1. Estimate a linear probability model of approve on white,
  1. What is the marginal impact of being white on the probability of acceptance?
  2. What is the estimated probability of a white person being accepted?
  3. What is the estimated probability of a non-white person being accepted?
  1. Estimate the same model using logit or probit. Estimate the marginal effects of the logit/probit model (use both APE and PEA methods). What is the marginal effect of being white on the probability of an accepted application using APE and PEA, and are these effects statistically significant?
  1. Now add the control variables hrat, obrat, loanprc, unem, male, married, dep, sch, cosign, chist, pubrec, mortlat1, and mortlat2 to both the linear probability model and logit or probit model. How do your estimates for the impact of being white on acceptance of an application from questions 5 and 6 change with control variables (be sure to discuss these estimates in terms of the marginal effects and statistical significance).
  1. For both the univariate model in 6 and the multivariate model in 7, calculate and report the McFadden pseudo R-squared.
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Modeling and Forecasting Homework 10 What is a linear probability model. Is there anything special one should do when estimating a linear probability model. What other models can we use if we have a dichotomous dependent variable. What are the advantages/disadvantages of linear probability models vis a vis the alternatives? Computer Exercise: Use the dataset loanapp to estimate the impact of race on the probability that a loan application is approved. Estimate a linear probability model of approve on white, What is the marginal impact of being white on the probability of acceptance? What is the estimated probability of a white person being accepted? What is the estimated probability of a non-white person being accepted? Estimate the same model using logit or probit. Estimate the marginal effects of the logit/probit model (use both APE and PEA methods). What is the marginal effect of being white on the probability of an accepted application using APE and PEA, and are these effects statistically significant? Now add the control variables hrat, obrat, loanprc, unem, male, married, dep, sch, cosign, chist, pubrec, mortlat1, and mortlat2 to both the linear probability model and logit or probit model. How do your estimates for the impact of being white on acceptance of an application from questions 5 and 6 change with control variables (be sure to discuss these estimates in terms of the marginal effects and statistical significance). For both the univariate model in 6 and the multivariate model in 7, calculate and report the McFadden pseudo R-squared.

 
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