Probit Regression

Probit regression is a statistical methodology developed for modeling binary outcomes, where the dependent variable can only take on values of 0 or 1. This model relies on the assumption that errors in the underlying binary data follow a normal distribution.

Its primary objective is to examine the relationship between a binary outcome variable and predictor variables. Probit regression is an effective tool for random effects models, particularly when dealing with moderate to large sample sizes. Widely applied in economics, social sciences, epidemiology, finance, and other fields, probit regression is especially pertinent when anticipated outcomes are dichotomous.

• Probit regression is a statistical method for modeling binary or dichotomous outcomes where the dependent variable can have only two possible values, i.e., 0 and 1.
• The key assumption in this regression model is that the errors reflect a normal distribution. The other assumptions are binary outcome, linearity, independence and no perfect multicollinearity.
• Although it provides a probabilistic framework for binary classification, it comes with computational and interpretational challenges.
• It differs from linear regression, which serves as an appropriate model for predicting numerical outcomes from continuous data series.

Probit regression facilitates modeling the relationship between the binary outcome variable and the predictor or independent variables employing the cumulative distribution function - standard normal distribution (also known as the probit function). The term 'probit' is derived from 'portmanteau', which refers to probability + unit. Thus, it measures the probability unit from the deviation of the outcome from the mean of the standard deviation.

The R probit regression formula is: E(Y|X)=P(Y=1|X)=Φ(β0+β1X)

When the cumulative standard normal distribution or probit function is denoted as Φ, the dependent variable Y is binary, and X denotes the predictors or independent variables. Thus, any change in X results in a change in the probability of Y. It estimates the effect of independent variables on the probability of an event occurring, with coefficients indicating changes in the z-score (standard normal distribution) for a one-unit change in the independent variable.

The model assumes that the linear combination of predictor variables and their coefficients is related to the latent variable, which is then transformed into a binary outcome using the probit function. It applies the maximum likelihood estimation to estimate the coefficients of the model. This technique is valuable when the relationship between the predictors and the binary outcome is non-linear and when the assumptions of logistic regression, another widely used method for binary outcomes, are not fulfilled.

Researchers use probit regression in fields like economics, finance, epidemiology, social sciences, etc., aiming to comprehend and predict binary outcomes based on multiple predictor variables.

Probit regression is an econometric measure that provides accurate and reliable statistical inferences based on the following assumptions:

1. Binary Outcome: The dependent variable must be binary, having two possible outcomes (generally coded as 0 and 1).
2. Errors are Independent: Observations must be independent, meaning one outcome's probability shouldn't rely on any other outcome in the data set to avoid clustering in the data.
3. Linearity: The outcome so acquired, and the independent or explanatory variables should possess a linear relationship.
4. No Perfect Multicollinearity: Independent variables shouldn't be perfectly correlated, as this can lead to estimation errors and influence the outcomes.
5. Normality of Errors: Probit regression assumes normally distributed errors, crucial for valid statistical inferences about model parameters.

Probit regression facilitates economic, financial, biological and epidemic research and analysis in real life. For instance, researchers often use it to model outcomes such as voting choices, disease occurrence, or response to a stimulus. Let us now discuss some examples:

Example #1 

Consider a study examining the factors influencing the likelihood of corporate bankruptcy using a probit regression model. Researchers may analyze variables such as leverage, liquidity, profitability, and industry-specific indicators to model the probability of a firm facing financial distress. The findings could reveal that high leverage levels and low profitability significantly increase the probability of bankruptcy. Such insights are crucial for investors, creditors, and policymakers to assess and manage financial risks in their portfolios, make informed lending decisions, and implement preventive measures to mitigate the economic impact of corporate failures.

Example #2

Suppose a bank aims to evaluate the probability of a borrower defaulting on a mortgage based on various financial indicators. The bank may use probit regression to model the relationship between binary outcomes (default or non-default) and predictors such as credit score, income, debt-to-income ratio (DTI), and other relevant financial variables.

By analyzing historical data on loan performance, the bank can estimate the coefficients in the probit model to understand the impact of each predictor on the likelihood of default. This information is valuable for making informed lending decisions, setting risk management strategies, and establishing appropriate loan terms for different applicants to minimize the banking portfolio's overall credit risk.

Probit regression is often used for binary classification problems, where the outcome variable has two possible outcomes. Here are some of its advantages and disadvantages:

#1 - Advantages

• Probit regression provides a probability of drawing results between 0 and 1, making it suitable for modeling binary outcomes.
• It considers that anomalies are normally distributed, allowing for more efficient analysis when this assumption holds.
• The model often fits the data well when the relationship between predictors and outcomes is based on normal distribution assumptions.
• Probit regression can provide marginal effects, indicating how a one-unit change in an independent variable affects the outcomes.

#2 - Disadvantages

• Probit regression involves intensive evaluation, particularly with large datasets, due to cumulative distribution functions and numerical optimization methods.
• Interpreting probit regression coefficients, which are available as z-scores, can be more challenging for individuals without a statistical background.
• Large sample sizes are required to estimate parameters accurately, especially for rare events, to avoid imprecise parameter estimates.
• Like other regression methods, probit regression is sensitive to multicollinearity, which can lead to unstable parameter estimates and affect interpretability.

Probit regression and linear regression are both statistical methods used to model relationships between variables, yet they are suitable for different data types and assumptions. Let us go through the distinctions between them:

This article has been a guide to what is Probit Regression Analysis. We compare it with logistic regression, & explain its examples, assumptions, advantages, & disadvantages. You may also take a look at the useful articles below –

• Multinomial Logistic Regression
• Tobit Regression
• Log-Linear Model