scholarly journals Binary Response Models: Logits, Probits and Semiparametrics

2001 ◽  
Vol 15 (4) ◽  
pp. 43-56 ◽  
Author(s):  
Joel L Horowitz ◽  
N.E Savin
Keyword(s):  
Probability Model ◽  
Binary Response ◽  
Linear Probability Model ◽  
Response Models ◽  
Explanatory Variables ◽  
Nonparametric Models ◽  
Binary Response Model ◽  
Unrealistic Assumption ◽  
Binary Response Models ◽  
Linear Probability

A binary-response model is a mean-regression model in which the dependent variable takes only the values zero and one. This paper describes and illustrates the estimation of logit and probit binary-response models. The linear probability model is also discussed. Reasons for not using this model in applied research are explained and illustrated with data. Semiparametric and nonparametric models are also described. In contrast to logit and probit models, semi- and nonparametric models avoid the restrictive and unrealistic assumption that the analyst knows the functional form of the relation between the dependent variable and the explanatory variables.

Binary response models comparison using the 𝛼-Chernoff divergence measure and exponential integral functions

2021 ◽  
pp. 37-53
Author(s):  
Subir Ghosh ◽  
Hans Nyquist
Keyword(s):  
Probability Distributions ◽  
Binary Response ◽  
Divergence Measure ◽  
Exponential Integral ◽  
Response Models ◽  
Content Type ◽  
Explanatory Variables ◽  
Criterion Functions ◽  
Models Comparison ◽  
Binary Response Models

In this paper, the families of binary response models are describing the data on a response variable having two possible outcomes and p p explanatory variables when the possible responses and their probabilities are functions of the explanatory variables. The α \alpha -Chernoff divergence measure and the Bhattacharyya divergence measure when α = 1 / 2 \alpha = 1/2 are the criterion functions used for quantifying the dissimilarity between probability distributions by expressing the divergence measures in terms of the exponential integral functions. The dependences of odds ratio and hazard function on the explanatory variables are also a part of the modeling.

Fragility of identification in panel binary response models

2019 ◽  
Vol 22 (3) ◽  
pp. 282-291
Author(s):  
Giovanni Forchini ◽  
Bin Jiang
Keyword(s):  
Simple Model ◽  
Random Effects ◽  
Binary Response ◽  
Response Model ◽  
Exogenous Variables ◽  
Response Models ◽  
Joint Distributions ◽  
Binary Response Model ◽  
Binary Response Models ◽  
Over Time

Summary The present paper considers a linear binary response model for panel data with random effects that differ across individuals but are constant over time, and it investigates the roles of the various assumptions that are used to establish conditions for identification. The paper also shows that even for this simple model, it is always possible—including in the logistic case—to find a distribution of the random effects given the exogenous variables, such that the slopes' parameters are arbitrarily different, but the joint distributions of the binary response variables are arbitrarily close.

Sibling Models, Categorical Outcomes, and the Intra-Class Correlation

2021 ◽  
Author(s):  
Richard Breen ◽  
John Ermisch
Keyword(s):  
Random Effects ◽  
Probability Model ◽  
Intraclass Correlation ◽  
Linear Probability Model ◽  
Intra Class Correlation ◽  
Sibling Differences ◽  
Sibling Models ◽  
Alternative Measures ◽  
Linear Probability ◽  
Parametric Estimate

Abstract In sibling models with categorical outcomes the question arises of how best to calculate the intraclass correlation, ICC. We show that, for this purpose, the random effects linear probability model is preferable to a random effects non-linear probability model, such as a logit or probit. This is because, for a binary outcome, the ICC derived from a random effects linear probability model is a non-parametric estimate of the ICC, equivalent to a statistic called Cohen’s κ. Furthermore, because κ can be calculated when the outcome has more than two categories, we can use the random effects linear probability model to compute a single ICC in cases with more than two outcome categories. Lastly, ICCs are often compared between groups to show the degree to which sibling differences vary between groups: we show that when the outcome is categorical these comparisons are invalid. We suggest alternative measures for this purpose.

Charged with Instability: Are Paternal Arrests and Criminal Charges Enough to Shake the Marital Stability of American Couples?

2020 ◽  
Vol 41 (12) ◽  
pp. 2423-2447
Author(s):  
Antonius D. Skipper ◽  
Douglas S. Bates ◽  
Zachary D. Blizard ◽  
Richard G. Moye
Keyword(s):  
Justice System ◽  
Probability Model ◽  
Well Being ◽  
Marital Stability ◽  
Fragile Families ◽  
Linear Probability Model ◽  
Paternal Incarceration ◽  
Criminal Charges ◽  
Linear Probability ◽  
The Relationship

With the growing rate of divorce, increasing efforts are being made to identify the factors that contribute to relationship dissolution for many American couples. One commonly noted, and particularly concerning, factor toward relationship instability is the incarceration of husbands and fathers. Although paternal incarceration and familial stability have been studied, little is known about the relationship between criminal charges and divorce. The current study utilized data from the Fragile Families and Child Wellbeing Study to understand the effect of paternal criminal charges on divorce for 725 families. Utilizing a logistic regression and two-stage least squares linear probability model, results show that, even without incarceration, being charged with a crime as a husband significantly increases the likelihood that a couple will get divorced. These findings have significant implications for understanding how encounters with the criminal justice system affect familial well-being and stability.

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