# Discussion 6

Chapter 9. Prediction for a Dichotomous Variable

Initial Postings: Read and reflect on the assigned readings for the week. Then post what you thought was the most important concept(s), method(s), term(s), and/or any other thing that you felt was worthy of your understanding in each assigned textbook chapter.Your initial post should be based upon the assigned reading for the week, so the textbook should be a source listed in your reference section and cited within the body of the text. Other sources are not required but feel free to use them if they aid in your discussion.

Also, provide a graduate-level response to each of the following questions:

1. In Chapter 9, the focus of study is the Dichotomous Variable. Briefly construct a model (example) to predict a dichotomous variable outcome. It can be something that you use at your place of employment or any example of practical usage. Please address each component of the discussion board. Also, cite examples according to APA standards.

[Your post must be substantive and demonstrate insight gained from the course material. Postings must be in the student’s own words – do not provide quotes!]

[Your initial post should be at least 450+ words and in APA format (including Times New Roman with font size 12 and double spaced). Post the actual body of your paper in the discussion thread then attach a Word version of the paper for APA review]

Prediction for Dichotomous Variable

Chapter 9

© 2019 McGraw-Hill Education. All rights reserved. Authorized only for instructor use in the classroom. No reproduction or distribution without the prior written consent of McGraw-Hill Education

Learning Objectives

Identify a limited dependent variable and its applications

Describe the linear probability model

Identify merits and shortcomings of the linear probability model

Model probit and logit models as determined by the realization of latent variable

Calculate marginal effects for logit and probit models

Execute estimation of a probit and logit model via maximum likelihood

Identify the merits and shortcomings of the probit and logit models in practice

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Limited Dependent Variable

Limited dependent variable

A dependent variable whose range of possible values has consequential constraints

Some constraints include upper and/or lower bounds or the ability to take on only discrete values

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IN THIS TABLE, A RANDOM VARIABLE SPENTit REPRESENTING THE AMOUNT OF MONEY SPENT BUYING PRODUCTS ONLINE BY HOUSEHOLD i IN WEEK k.

PRODUCTS ESSENTIALLY ALWAYS HAVE NON-NEGATIVE PRICES, SO THIS RANDOM VARIABLE IS CONSTRAINED TO BE AT LEAST ZERO.

IN THIS TABLE, WE SEE SEVERAL OBSERVATIONS WITH SPENTit VALUES EXACTLY EQUALS THE CONSTRAINT OF ZERO.

Limited Dependent Variables

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Dichotomous (or binary) dependent variable
A limited dependent variable that can take on just two values, typically recorded as 0 and 1
Measure many different types of outcomes: purchase/don’t purchase, project success/project failure, employed/unemployed, approve/disapprove
Limited Dependent Variables

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Linear probability model defined as regression analysis applied to a dichotomous dependent variable
Widely used model
The act of fitting the equation Purchase = α + βSubFee to the data by solving the moment condition is an application of a linear probability model.

The Linear Probability Model

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Data on Subscription Fees and Purchase Decisions for SaferContent

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The following table shows regression estimates that fit the function Purchase = α + βSubFee to the data:

Based on the estimates in the above table the determining function would be: Purchasei = 1.65 – 0.05 × SubFeei

The Linear Probability Model

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If we assume the data-generating process for Y to be:
Y1 = α + β1X1i + … + βKXKi + Ui
Y is a dichotomous dependent variable
THEN:
Β1, …, βK represent the change in the probability of Y equaling one with a one unit increase in X1, … ,XK (respectively), holding all other Xs constant. For example, we can express β1 as: β1= Pr(Y = 1|X1 + 1, X2, …, XK) – Pr(Y = 1|X1 + 1, X2, …, XK)
The Linear Probability Model

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Merits
Imposes no restrictions on the associated regression analysis, so all methods discussed earlier (use of dummy variables, selecting controls, instrumental variables, panel data methods) seamlessly apply.
Shortcomings
It ignores the limitation of the dependent variable
The lack of restrictions on the range of predicted values of the outcome

Merits and Shortcomings of the Linear Probability Model

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Limit-violating prediction is a predicted value for a limited dependent variable that does not fall within that variable’s limits
For many applications, limit-violating predictions may not be a problem in practice
Could engineer the Xs in such a way as to preclude predictions of Y outside of 0-1
Merits and Shortcomings of the Linear Probability Model

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To overcome the limitations of the linear probability model, probit and logit models are used
The choice between a linear probability model and the alternative models is not obvious
There is no universally ”right” model
Probit and Logit Models

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The key difference the linear probability model and the alternative models (logit and probit models) is the connection between the determining function, the unobservables, and the dependent variables.
Purchase = α + βSubFee
Two Shortcomings of the linear probability model:
It is hard to believe that the determining function and unobservables always add up to exactly 0 or 1
Predictions about the effect of subscription fee (SubFee) on purchases may be unrealistic

Probit and Logit Models

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Rather than setting the dependent variable equal to the sum of the determining function and the unobservables, let the value of the dependent variable depend on this sum but in a coarse way
The sum of the determining function and the unobservables equal a latent variable
A latent variable is a variable that cannot be observed, but information about it can be inferred from other observed variables

Probit and Logit Models

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We define the sum of defining function ( α + βSubFeei) and the unobservables (Ui) to be a latent variable
If we call latent variable Utility, then:
Utilityi = α + βSubFeei + Ui
We assume a purchase occurs if utility is positive (> 0) and a purchase does not occur if utility is not positive (≤ 0)
We can express the purchase decision as:
Purchasei =

Probit and Logit Models

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Examples of Dichotomous Dependent Variables Coupled with Latent Variables

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Define the latent variable, Y*, as the sum of the determining function and the unobservables:
Y*i = α + β1X1i + … + βKXKi + Ui
Then define the dependent variable, Yi , to be 1 if the latent variable exceeds 0, and otherwise:
Yi =
Notice we do not need the determining function and unobservables to add up exactly to 0 or 1
Probit and Logit Models

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The latent variable formation for Y also prevents unreasonable predictions about the probability of Y equaling 1
Pr(Yi = 1|X1i, …, XKi) = Pr(Y*i > 0|X1i , …, XKi)
This equation states that the probability the outcome (Y) equals 1, given the values for the Xs, is equal to the probability that the latent variable (Y*) is greater than 0, given the values for the Xs
Probit and Logit Models

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Probit and Logit Models
Pr(Yi = 1|X1i, …, XKi) = Pr(α + β1X1i + … + βKXKi + Ui > 0|X1i , …, XKi)
Pr(Yi = 1|X1i, …, XKi) = Pr(Ui > ‒ α ‒ β1X1i ‒ … ‒ βKXKi|X1i , …, XKi)
While the determining function is unconstrained, the probability that Y equals 1 is explicitly defined to be a probability in terms of U, so constrained to be between 0 and 1

‹#›

Probit model a latent variable formulation for a dichotomous dependent variable that assumes a standard normal distribution for the unobservables
The probability that Y equals 1 for given values of the Xs using formula:
Pr(Yi = 1|X1i, …, XKi) = Pr(Ui > ‒ α ‒ β1X1i ‒ … ‒ βKXKi|X1i , …, XKi)

Probit and Logit Models

‹#›

Probit and Logit Models
Probability Y Equals 1 for Given Xs, Assuming Standard Normal Distribution for U

IN THE GRAPH ϕ(U) IS THE PROBABILITY DENSITY FUNCTION (pdf) FOR THE STANDARD NORMAL DISTRIBUTION.

‹#›

Probit and Logit Models
Since we know Ui is a standard normal random variable, simplify the expression for Pr(Yi = 1|X1i, …, XKi)
Normal distribution is symmetric around its mean (which is 0 for U)
Pr(Ui > ‒ α ‒ β1X1i ‒ … ‒ βKXKi|X1i , …, XKi) =

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