Background
A probit model is paramount in understanding discrete choice situations in econometrics. It is extensively used in statistical analyses where the dependent variable is dichotomous, such as a yes/no decision or a success/failure outcome.
Historical Context
The probit model was developed by Chester Ittner Bliss in 1934. Initially used in bioassay, it has since found extensive applications across various fields, including economics, where it models binary and ordinal response variables.
Definitions and Concepts
The probit model is fundamentally grounded in probability theory, making use of a cumulative normal distribution function to link the latent variables to the observed binary outcomes. In this context:
- Discrete Choice Model: A framework where individual choices among a set of finite alternatives are analyzed.
- Cumulative Normal Distribution: Utilized to transform a linear combination of predictors into probabilities constrained between 0 and 1.
Major Analytical Frameworks
Classical Economics
In classical economics, choice modeling often ties into utility theory, predicting binary decisions aligned with individual benefit maximization.
Neoclassical Economics
Keynesian Economics
Keynesian models may incorporate probit analysis in examining consumer preferences under varying macroeconomic conditions.
Marxian Economics
Institutional Economics
Behavioral Economics
Probit models in behavioral economics help in understanding decision-making processes and deviations from rational choice theory.
Post-Keynesian Economics
Austrian Economics
Development Economics
Development economists frequently use probit models to evaluate policy impacts on discrete outcomes, such as access to education or healthcare service success rates.
Monetarism
Comparative Analysis
When compared to logit models, another form of discrete choice modeling, the probit model assumes normally distributed errors which may lead to different inferential properties and interpretability nuances. While both models typically offer similar outputs, they diverge in their mathematical underpinnings and specific applicability contexts.
Case Studies
- Consumer Choice Analysis: Probit models can analyze consumer choices regarding product purchases based on advertising exposure and demographic factors.
- Public Policy Evaluation: Efficacy of public health interventions, like vaccination programs, often leverages probit models to determine impact distinctions between exposed and non-exposed populations.
Suggested Books for Further Studies
- Econometric Analysis by William H. Greene
- Microeconometrics: Methods and Applications by A. Colin Cameron and Pravin K. Trivedi
- Discrete Choice Methods with Simulation by Kenneth E. Train
Related Terms with Definitions
Logit Model: A discrete choice model similar to the probit model but using a logistic function for the distribution of the stochastic error term as opposed to the normal distribution in a probit model.
Latent Variable: An unobserved variable that influences observable outcomes, often modeled in probit analysis through a normally distributed error term.
Binary Outcome: A result that can take on one of two possible states, typically coded as 0 or 1 in econometric modeling.
