INTRODUCTION TO ECONOMETRICS II ECO 306 NOUN 138 5.2.4.0 SUMMARY In this unit, we started with the linear probability model being the simplest binary choice model where the probability of the event occurring is assumed to be a linear function of a set of descriptive variables. We then proceeded to goodness of fit and statistical tests using maximum likelihood estimation as a method of estimating the parameters of a model given observations, by finding the parameter values that maximise the likelihood of making the observations given the parameters. 5.2.5.0 CONCLUSION Although numerous measures have been proposed for comparing alternative model specifications, there is still no measure of goodness of fit equivalent to maximum likelihood estimation. The students should be of the opinion that every of the estimation measure has its shortcomings and it is recommended to consider more than one and compare their results. 5.2.6.0 TUTOR-MARKED ASSIGNMENT A researcher, using a sample of 2,868 individuals from the NLSY (National Longitudinal Survey of Young Men, is investigating how the probability of a respondent obtaining a bachelors degree from a four-year college is related to the respondents score on ASVABC. 26.7 percent of the respondents earned bachelors degrees. ASVABC ranged from 22 to 65, with mean value 50.2, and most scores were in the range 40 to 60. Defining a variable BACH to be equal to 1 if the respondent has a bachelors degree (or higher degree) and 0 otherwise, the researcher fitted the OLS regression (standard errors in parentheses ̂ SEE. (0.042) (0.001) The researcher also fitted the following logit regression SEE. (0.487) (0.009) whereZ is the variable in the logit function. Using this regression, the researcher plotted the probability and marginal effect functions shown in the diagram below.
