Binary choice models occur frequently in economic modeling. A measure of the predictive performance of binary choice models that is often reported is the hit rate of a model. This paper develops a test for the outperformance of a predictor for binary outcomes over a naive prediction method, which predicts the outcome that is most often observed. This is done for a general class of prediction models, including the well known Probit and Logit models. In many cases the test is easy to compute. The test is then applied and compared to a general test of Pesaran and Timmermann (1992) for dependence between predictors and realizations
Multi-class predictive models are generally evaluated averaging binary classification indicators wit...
Many statistical analyses are performed by means of a regression model. These models investigate the...
Bayesian models use posterior predictive distributions to quantify the uncertainty of their predicti...
A binary response model is a regression model in which the dependentvariable Y is a binary random va...
Although semiparametric alternatives are available, parametric binary choice models are widely used ...
We provide statistical inference for measures of predictive success. These measures are frequently u...
The following thesis compares the performance of several parametric and semiparametric estimators in...
A measure of probability changes in probit and logit dichotomous models is proposed based of the eff...
We address the issue of using a set of covariates to categorize or predict a binary outcome. This is...
Modern data science tools are effective to produce predictions that strongly correlate with response...
Evaluating the performance of models predicting a binary outcome can be done using a variety of meas...
There is a vast literature that has been focusing on testing the forecasting performance of various ...
This Master thesis investigates the semi-parametric estimation method Maximum Score of Manski (1988)...
Strong assumptions needed to correctly specify parametric binary choice probability models make them...
Mixed Logit model (MXL) is generated from Multinomial Logit model (MNL) for discrete, i.e. nominal...
Multi-class predictive models are generally evaluated averaging binary classification indicators wit...
Many statistical analyses are performed by means of a regression model. These models investigate the...
Bayesian models use posterior predictive distributions to quantify the uncertainty of their predicti...
A binary response model is a regression model in which the dependentvariable Y is a binary random va...
Although semiparametric alternatives are available, parametric binary choice models are widely used ...
We provide statistical inference for measures of predictive success. These measures are frequently u...
The following thesis compares the performance of several parametric and semiparametric estimators in...
A measure of probability changes in probit and logit dichotomous models is proposed based of the eff...
We address the issue of using a set of covariates to categorize or predict a binary outcome. This is...
Modern data science tools are effective to produce predictions that strongly correlate with response...
Evaluating the performance of models predicting a binary outcome can be done using a variety of meas...
There is a vast literature that has been focusing on testing the forecasting performance of various ...
This Master thesis investigates the semi-parametric estimation method Maximum Score of Manski (1988)...
Strong assumptions needed to correctly specify parametric binary choice probability models make them...
Mixed Logit model (MXL) is generated from Multinomial Logit model (MNL) for discrete, i.e. nominal...
Multi-class predictive models are generally evaluated averaging binary classification indicators wit...
Many statistical analyses are performed by means of a regression model. These models investigate the...
Bayesian models use posterior predictive distributions to quantify the uncertainty of their predicti...