Introduction to econometrics II eco 356 faculty of social sciences course guide course Developers: Dr. Adesina-Uthman



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Introduction to Econometrics ECO 356 Course Guide and Course Material
Estimation
CONTENTS
5.2.1.0 Introduction
5.2.2.0 Objectives
5.2.3.0 Main Content
5.2.3.1 The Linear Probability Model
5.2.3.2 Goodness of Fit and Statistical Tests
5.2.4.0 Summary
5.2.5.0 Conclusion
5.2.6.0 Tutor-Marked Assignment
5.2.7.0 References/Further Reading
5.2.1.0 INTRODUCTION
Most times economists are known to be interested in the factors behind the decision- making of individuals or enterprises. Examples are
- Why do some people go to college while others do not
- Why do some women enter the labour force while others do not
- Why do some people buy houses while others rent
- Why do some people migrate while others stay put Models have been developed to proffer solutions to these interest, and they are known as abinary choice or qualitative response models. The outcome will be denoted by Y, and assigned a value of 1 if the event occurs and 0 otherwise. Models with more than two possible outcomes have also been developed, but let us restrict our scope to abinary choice. The linear probability model apart, binary choice models are fitted using maximum likelihood estimation.
5.2.2.0 OBJECTIVE
The main objective of this unit is to provide the students with a clear understanding that apart from the linear probability model, binary choice models are fitted using maximum likelihood estimation.


INTRODUCTION TO ECONOMETRICS II

ECO 306

NOUN
135




5.2.3.0 MAIN CONTENTS
5.2.3.1 The Linear Probability Model
The simplest binary choice model is the linear probability model whereas the name implies, the probability of the event occurring, p, is assumed to be a linear function of a set of descriptive variables. That is
(
)
…[5.09] For one descriptive variable, the relationship is as shown in Figure 5.1. Of course,p is unobservable, and as expected there is only one data Y, on the outcome. In the linear probability model, this is used as a dummy variable for the dependent variable.
Figure 5.1. Linear Probability Model
Regrettably, the linear probability model though simple still has some serious defects. First, there are problems with the disturbance term. As usual, the value of the dependent variable in observation i,has a nonstochastic component and a random component. The nonstochastic component depends on and the parameters and is the expected value of given
|
). The random component is the disturbance term.
(
|
)
…[5.10]



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