We address the issue of using a set of covariates to categorize or predict a binary outcome. This is a common problem in many disciplines including economics. In the context of a prespecified utility (or cost) function we examine the construction of fore-casts suggesting an extension of the Manski (1975, 1985) maximum score approach. We provide analytical properties of the method and compare it to more common ap-proaches such as forecasts or classifications based on conditional probability models and discriminant analysis. The results are informative for both forecasting environ-ments as well as program allocation where the value of including the participant in th
This paper discusses an approach to evaluating a broader class of pre-dictions than traditionally ha...
Propensity scores are often used for stratification of treatment and control groups of subjects in o...
Making predictions nowadays is of high importance for any company, whether small or large, as thanks...
We address the issue of using a set of covariates to categorize or predict a binary outcome. This is...
We address the issue of using a set of covariates to categorize or predict a binary outcome. This is...
Chapter I of this dissertation addresses the problem of optimally forecasting a binary variable base...
We consider constructing probability forecasts from a parametric binary choice model under a large f...
In this paper we address the problem of optimally forecasting a binary variable for a het-erogeneous...
Binary choice models occur frequently in economic modeling. A measure of the predictive performance ...
Good prediction methods are important in many fields where qualitative variables are involved. The c...
We present a visual method for assessing the predictive power of models with binary outcomes. This t...
In a seminal paper, Manski (1975) introduces the Maximum Score Estimator (MSE) of the structural par...
This paper considers the role of covariates when using predicted probabilities to interpret main eff...
De Finetti introduced the concept of coherent previsions and conditional pre-visions through a gambl...
This Master thesis investigates the semi-parametric estimation method Maximum Score of Manski (1988)...
This paper discusses an approach to evaluating a broader class of pre-dictions than traditionally ha...
Propensity scores are often used for stratification of treatment and control groups of subjects in o...
Making predictions nowadays is of high importance for any company, whether small or large, as thanks...
We address the issue of using a set of covariates to categorize or predict a binary outcome. This is...
We address the issue of using a set of covariates to categorize or predict a binary outcome. This is...
Chapter I of this dissertation addresses the problem of optimally forecasting a binary variable base...
We consider constructing probability forecasts from a parametric binary choice model under a large f...
In this paper we address the problem of optimally forecasting a binary variable for a het-erogeneous...
Binary choice models occur frequently in economic modeling. A measure of the predictive performance ...
Good prediction methods are important in many fields where qualitative variables are involved. The c...
We present a visual method for assessing the predictive power of models with binary outcomes. This t...
In a seminal paper, Manski (1975) introduces the Maximum Score Estimator (MSE) of the structural par...
This paper considers the role of covariates when using predicted probabilities to interpret main eff...
De Finetti introduced the concept of coherent previsions and conditional pre-visions through a gambl...
This Master thesis investigates the semi-parametric estimation method Maximum Score of Manski (1988)...
This paper discusses an approach to evaluating a broader class of pre-dictions than traditionally ha...
Propensity scores are often used for stratification of treatment and control groups of subjects in o...
Making predictions nowadays is of high importance for any company, whether small or large, as thanks...