Estimators of regression coefficients are known to be asymptotically normally distributed, provided certain regularity conditions are satisfied. In small samples and if the noise is not normally distributed, this can be a poor guide to the quality of the estimators. The paper addresses this...

We study optimality properties in finite samples for time-varying volatility models driven by the score of the predictive likelihood function. Available optimality results for this class of models suffer from two drawbacks. First, they are only asymptotically valid when evaluated at the...

The most important financial source for behavioral economics is the Russell Sage Foundation (RSF). The most prominent behavioral economists among the RSF’s twenty-six member Behavioral Economics Roundtable (BER) are Kahneman, Tversky, Thaler, Camerer, Loewenstein, Rabin, and Laibson. The...

The origin of prospect theory is the desire to test the intuitive statistician in the real world. The development of this theory by the cognitive psychologists Kahneman and Tversky can be traced to the former's work in cognitive psychophysics, in which deviations from average behavior are termed...

This paper provides an overview of the work of Gigerenzer, thereby focusing on his criticisms of the Heuristics and Biases theory of Kahneman and Tversky. It is proposed that Gigerenzer's work can be both thematically and chronologically organized as: historical research on statistics =...

We introduce a new estimation framework which extends the Generalized Method of Moments (GMM) to settings where a subset of the parameters vary over time with unknown dynamics. To filter out the dynamic path of the time-varying parameter, we approximate the dynamics by an autoregressive process...

While the stochastic volatility (SV) generalization has been shown to improvethe explanatory power compared to the Black-Scholes model, the empiricalimplications of the SV models on option pricing have not been adequately tested.The purpose of this paper is to first estimate a multivariate SV...

We propose a new class of observation driven time series models referred to as Generalized Autoregressive Score (GAS) models. The driving mechanism of the GAS model is the scaled score of the likelihood function. This approach provides a unified and consistent framework for introducing...

We propose a new class of observation-driven time-varying parameter models for dynamic volatilities and correlations to handle time series from heavy-tailed distributions. The model adopts generalized autoregressive score dynamics to obtain a time-varying covariance matrix of the multivariate...

Market share models for weekly store-level data are useful to understand competitive structures by delivering own and cross price elasticities. These models can however not be used to examine which brands lose share to which brands during a specificperiod of time. It is for this purpose that we...