The influence of fear in decisions: Experimental evidence

This article studies decisions made under conditions of fear, when a catastrophic outcome is introduced in a lottery. It reports on experimental results and seeks to compare the predictions of the expected utility (EU) framework with those of a new axiomatic treatment of choice under uncertainty that takes explicit account of emotions such as fear (Chichilnisky 1996, 2000, 2002, 2009). Results provide evidence that fear influences the cognitive process of decision-making by leading some subjects to focus excessively on catastrophic events. Such heterogeneity in subjects’ behavior, while not consistent with EU-based functions, is fully consistent with the new type of utility function implied by the new axioms.     

Optimal Use of Communication Resources

We study a repeated game with asymmetric information about a dynamic state of nature. In the course of the game, the better‐informed player can communicate some or all of his information to the other. Our model covers costly and/or bounded communication. We characterize the set of equilibrium payoffs and contrast these with the communication equilibrium payoffs, which by definition entail no communication costs.     

Likelihood-based estimation for Gaussian MRFs

Markov random fields (MRFs) express spatial dependence through conditional distributions, although their stochastic behavior is defined by their joint distribution. These joint distributions are typically difficult to obtain in closed form, the problem being a normalizing constant that is a function of unknown parameters. The Gaussian MRF (or conditional autoregressive model) is one case where the normalizing constant is available in closed form; however, when sample sizes are moderate to large (thousands to tens of thousands), and beyond, its computation can be problematic. Because the conditional autoregressive (CAR) model is often used for spatial-data modeling, we develop likelihood-inference methodology for this model in situations where the sample size is too large for its normalizing constant to be computed directly. In particular, we use simulation methodology to obtain maximum likelihood estimators of mean, variance, and spatial-depencence parameters (including their asymptotic variances and covariances) of CAR models.     

Evaluating the Move to a Linear Tax System in Germany and Other European Countries

This paper proposes a comparison of the results of tax policy analysis obtained on the basis of unitary and collective representations of the household. We first generate labour supplies consistent with the collective rationality, by use of a model calibrated on microdata as described in Vermeulen et al. [Collective Models of Household Labor Supply with Nonconvex Budget Sets and Nonparticipation: A Calibration Approach (2006)]. A unitary model is then estimated on these collective data and unitary and collective responses to a tax reform are compared. We focus on the introduction of linear taxation in Germany. The exercise is replicated for other European countries and other topical reforms. Distortions due to the use of a unitary model turn out to be important in predicting labour supply adjustments, in the design of tax revenue neutral reforms, and in predicting a reform’s welfare implications.

Relative effect sizes for measures of risk

Effect sizes are an important component of experimental design, data analysis, and interpretation of statistical results. In some situations, an effect size of clinical or practical importance may be unknown to the researcher. In other situations, the researcher may be interested in comparing observed effect sizes to known standards to quantify clinical importance. In these cases, the notion of relative effect sizes (small, medium, large) can be useful as benchmarks. Although there is generally an extensive literature on relative effect sizes for continuous data, little of this research has focused on relative effect sizes for measures of risk that are common in epidemiological or biomedical studies. The aim of this paper, therefore, is to extend existing relative effect sizes to the relative risk, odds ratio, hazard ratio, rate ratio, and Mantel–Haenszel odds ratio for related samples. In most scenarios with equal group allocation, effect sizes of 1.22, 1.86, and 3.00 can be taken as small, medium, and large, respectively. The odds ratio for a non rare event is a notable exception and modified relative effect sizes are 1.32, 2.38, and 4.70 in that situation.     

Simplified Likelihood Based Goodness-of-fit Tests for the Weibull Distribution

The aim of this paper is to present new likelihood based goodness-of-fit tests for the two-parameter Weibull distribution. These tests consist in nesting the Weibull distribution in three-parameter generalized Weibull families and testing the value of the third parameter by using the Wald, score, and likelihood ratio procedures. We simplify the usual likelihood based tests by getting rid of the nuisance parameters, using three estimation methods. The proposed tests are not asymptotic. A comprehensive comparison study is presented. Among a large range of possible GOF tests, the best ones are identified. The results depend strongly on the shape of the underlying hazard rate.     

Risk aversion and equilibrium selection in a vertical contracting setting: an experiment

Theory and Decision - The theoretical literature on vertical relationships usually assumes that beliefs about secret contracts take specific forms. In a recent paper, Eguia et al. (Games Econ Behav...