Econometrics 2 for Economics and Finance - Topic 4: Binary Outcome Models

These flashcards cover key terms and concepts related to binary outcome models, including definitions and their significance in the field of econometrics.

Binary Outcome Models

Models in which the dependent variable is qualitative, specifically taking on two values (e.g., 0/1).

Linear Probability Model (LPM)

A type of regression model where the dependent variable is binary, interpreting the coefficients as probabilities.

Logit Model

A binary outcome model where the probability of success is modeled using the logistic function.

Probit Model

A binary outcome model where the probability of success is modeled using the standard normal cumulative distribution function.

Marginal Effects (MEs)

The change in the probability of the dependent variable occurring as a result of a one-unit change in an independent variable.

Maximum Likelihood Estimation (MLE)

A method used to estimate the parameters of a statistical model that maximizes the likelihood function.

Goodness-of-Fit

A measure to determine how well a statistical model fits a set of observations.

Odds Ratio

A measure of the association between an exposure and an outcome, representing the odds that an outcome will occur with an exposure compared to the odds of the outcome occurring without the exposure.

Cumulative Distribution Function (CDF)

A function that describes the probability that a random variable takes on a value less than or equal to a given value.

Heteroskedasticity

A condition in which the variance of the errors varies across observations in a regression model.