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

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

The Econometric Analysis of Microscopic Simulation Models

Microscopic simulation models are often evaluated based on visual inspection of the results.This paper presents formal econometric techniques to compare microscopic simulation (MS) models with real-life data.A related result is a methodology to compare different MS models with each other.For this purpose, possible parameters of interest, such as mean returns, or autocorrelation patterns, are classified and characterized.For each class of characteristics, the appropriate techniques are presented.We illustrate the methodology by comparing the MS model developed by Levy, Levy, and Solomon (2000) and the market fraction model developed by He and Li (2005a, b) with actual dataMicroscopic simulation models;Econometric analysis

Estimating Risk Attitudes Using Lotteries; A Large Sample Approach

Attitudes towards risk play a major role in many economic decisions. In empirical studies one quite often assumes that attitudes towards risk do not vary across individuals. This papers questions this assumption and analyses which factors influence an individual's risk attitude. Based on questions on lotteries in a large household survey we semiparametrically estimate an index for risk aversion. We only make weak assumptions about the underlying deci- sion process, and our estimation method allows for generalisations of expected utility. We find strong links between risk aversion and gender, education level, and income of the individual. We also estimate a structural model based on Cumulative Prospect Theory and find that the value function depends on an index that is very similar to the index of risk aversion. Expected utility is strongly rejected and the probability weighting function varies significantly with gender, age, and income of the individual.Risk aversion;non-expected utility;semiparametric estimation

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

The Econometric Analysis of Microscopic Simulation Models

Microscopic simulation models are often evaluated based on visual inspection of the results.This paper presents formal econometric techniques to compare microscopic simulation (MS) models with real-life data.A related result is a methodology to compare different MS models with each other.For this purpose, possible parameters of interest, such as mean returns, or autocorrelation patterns, are classified and characterized.For each class of characteristics, the appropriate techniques are presented.We illustrate the methodology by comparing the MS model developed by Levy, Levy, and Solomon (2000) and the market fraction model developed by He and Li (2005a, b) with actual data

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.

Estimating Risk Attitudes Using Lotteries; A Large Sample Approach

Attitudes towards risk play a major role in many economic decisions. In empirical studies one quite often assumes that attitudes towards risk do not vary across individuals. This papers questions this assumption and analyses which factors influence an individual's risk attitude. Based on questions on lotteries in a large household survey we semiparametrically estimate an index for risk aversion. We only make weak assumptions about the underlying deci- sion process, and our estimation method allows for generalisations of expected utility. We find strong links between risk aversion and gender, education level, and income of the individual. We also estimate a structural model based on Cumulative Prospect Theory and find that the value function depends on an index that is very similar to the index of risk aversion. Expected utility is strongly rejected and the probability weighting function varies significantly with gender, age, and income of the individual.