Econometrics

Use logit

models whenever your dependent

variable is binary (also called dummy) which

takes values 0 or 1.

Logit regression is a nonlinear regression model

that forces the output (predicted values) to be

either 0 or 1.

Logitmodels estimate the probability of your

dependent variable to be 1 (

Y=1). This is the probability that some event happens.

Coefficient. An increase in x increases/decreases the likelihood that y=1 (makes that outcome more/less likely).  In other words, an increase in x makes the outcome of 1 more or less likely.  We interpret the sign of the coefficient but not the magnitude.  The magnitude cannot be interpreted using the coefficient because different models have different scales of coefficients.  

Interpretation of marginal effects  An increase in x increases (decreases) the probability that y=1 by the marginal effect expressed as a percent.  o For dummy independent variables, the marginal effect is expressed in comparison to the base category (x=0). o For continuous independent variables, the marginal effect is expressed for a one-unit change in x.

OLS model shows us that (only)….. variables are statistically significant for this model. …. Have positive coefficient and are statistically significant. What is more, these variables are statistically significant only at…. % significance level. ….. is positive but statistically insignificant for this model.

R2 tells you how well your independent variables explain dependent variable.

The results of R-squared shows that data fits to the model or in the other words, the independent variables explain dependent variable in …. % ( In ….% is explain by different other factors)

If the “ dependent variable” changes by 1 (unit %) independent variable increases by B1 unit.

According to the results from the OLS Regression, only one independent variable is statistically significant at the 5% level. Rest of them shows very strong correlation at the 1% level. The results of R-squared shows that data fits to the model which means that independent variables explain the model in 81,7%.