Testing predictive performance of binary choice models

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.Marketing;Predictive performance;Binary choice;Testing

Testing for Mean-Coherent Regular Risk Spanning

Coherent risk measures have received considerable attention in the recent literature.Coherent regular risk measures form an important subclass: they are empirically identifiable, and, when combined with mean return, they are consistent with second order stochastic dominance.As a consequence, these risk measures are natural candidates in a mean-risk trade-off portfolio choice.In this paper we develop a mean-coherent regular risk spanning test and related performance measure.The test and the performance measure can be implemented by means of a simple semi-parametric instrumental variable regression, where instruments have a direct link with the stochastic discount factor.We illustrate applications of the spanning test and the performance measure for several coherent regular risk measures, including the well known expected shortfall.portfolio choice;coherent risk;spanning test

Maximum score estimation in the ordered response model

Estimation

Parametric and semi-parametric modelling of vacation expenditures

Consumer Choice;Recreation

Semi-parametric estimation of the sample selection model

Wages;Estimation;Models

The Non- and Semiparametric Analysis of MS Models: Some Applications

This paper illustrates how to compare different microscopic simulation (MS) models and how to compare a MS model with real data in case the parameters of interest are estimated non- or semiparametrically.As examples we investigate the marginal single-period probability density function of stock returns, and the corresponding spectral density function and memory parameters.We illustrate the methodology by the MS models developed by Levy, Levy, Solomon (2000) and the market fraction model developed by He and Li (2005a, b), and confront the resulting return data with the S&P 500 stock index data.Microscopic simulation models;Probability density function;Spectral density function;Memory parameters

Global Warming and Local Dimming: The Statistical Evidence

Global warming;Dimming;Aerosols;Dynamic panel data