Beliefs about overconfidence

This experiment elicits beliefs about other people’s overconfidence and abilities. We find that most people believe that others are unbiased, and only few think that others are overconfident. There is a remarkable heterogeneity between these groups. Those people who think others are underconfident or unbiased are overconfident themselves. Those who think others are overconfident are underconfident themselves. Despite this heterogeneity, people overestimate on average the abilities of others as they do their own ability. One driving force behind this result is the refusal to process information about oneself: not only does this lead to overestimation of one’s own ability, but by means of social projection also to overestimation of others’ abilities.     

Beneficial safety decreases

We construct a model of rational choice under risk with biased risk judgement. On its basis, we argue that sometimes, a regulator aiming at maximising social welfare should affect the environment in such a way that it becomes ‘less safe’ in common perception. More specifically, we introduce a bias into each agent’s choice of optimal risk levels: consequently, in certain environments, agents choose a behaviour that realises higher risks than intended. Individuals incur a welfare loss through this bias. We show that by deteriorating the environment, the regulator can motivate individuals to choose behaviour that is less biased, and hence realises risk levels closer to what individuals intended. We formally investigate the conditions under which such a Beneficial Safety Decrease—i.e. a deteriorating intervention that has a positive welfare effect—exists. Finally, we discuss three applications of our model.     

Does product complexity matter for competition in experimental retail markets?

We describe a first experiment on whether product complexity affects competition and consumers in retail markets. We are unable to detect a significant effect of product complexity on prices, except insofar as the demand elasticity for complex products is higher. However, there is qualified evidence that complex products have the potential to induce consumers to buy more than they would otherwise. In this sense, consumer exploitability in quantities cannot be ruled out. We also find evidence for shaping effects: consumers’ preferences are shaped by past experience with prices, and firms may in principle exploit this to sell more.     

Digit ratios (2D:4D) as predictors of risky decision making for both sexes

Many important decisions involve financial risk, and substantial evidence suggests that women tend to be more risk averse than men. We explore a potential biological basis of risk-taking variation within and between the sexes by studying how the ratio between the length of the second and fourth fingers (2D:4D) predicts risk-taking. A smaller 2D:4D ratio has been linked to higher exposure to prenatal testosterone relative to estradiol, with men having lower ratios than women. In financially motivated decision-making tasks, we find that men and women with smaller 2D:4D ratios chose significantly riskier options. We further find that the ratio partially explains the variation in risk-taking between the sexes. Moreover, for men and women at the extremes of the digit-ratio distribution the difference in risk-taking disappears. Thus, the 2D:4D ratio partially explains variation in financial risk-taking behavior within and between sexes and offers evidence of a biological basis for risk-taking behavior.     

A Diamond-Stiglitz approach to the demand for self-protection

The existing research concerning the relationship between risk aversion and prudence and the demand for self-protection assumes that the loss variable follows a Bernoulli distribution, and that changes in the level of self-protection are mean preserving. The analysis here replaces these two very strong conditions with ones which are more general. When doing this, the method of analysis is also significantly modified. This modification includes representing a change in the level of self-protection using the procedure developed by Diamond and Stiglitz (Journal of Economic Theory 8:337-360, 1974) for representing a change in risk. This alternate representation allows the existing findings to be generalized considerably, and also simplifies the analysis.     

Two-sided intergenerational moral hazard,long-term care insurance,and nursing home use

Two-sided intergenerational moral hazard occurs (i) if the parent’s decision to purchase long-term care (LTC) coverage undermines the child’s incentive to exert effort because the insurance protects the bequest from the cost of nursing home care, and (ii) when the parent purchases less LTC coverage, relying on child’s effort to keep him out of the nursing home. However, a “net” moral hazard effect obtains only if the two players’ responses to exogenous shocks fail to neutralize each other, entailing a negative relationship between child’s effort and parental LTC coverage. We focus on outcomes out of equilibrium, interpreting them as a break in the relationship resulting in no informal care provided and hence high probability nursing home admission. Changes in the parent’s initial wealth, LTC subsidy received, and child’s expected inheritance are shown to induce “net” moral hazard, in contradistinction to changes in child’s opportunity cost and share in the bequest.     

Data skeletons: simultaneous estimation of multiple quantiles for massive streaming datasets with applications to density estimation

We consider the problem of density estimation when the data is in the form of a continuous stream with no fixed length. In this setting, implementations of the usual methods of density estimation such as kernel density estimation are problematic. We propose a method of density estimation for massive datasets that is based upon taking the derivative of a smooth curve that has been fit through a set of quantile estimates. To achieve this, a low-storage, single-pass, sequential method is proposed for simultaneous estimation of multiple quantiles for massive datasets that form the basis of this method of density estimation. For comparison, we also consider a sequential kernel density estimator. The proposed methods are shown through simulation study to perform well and to have several distinct advantages over existing methods.     

A multivariate version of Gini's rank association coefficient

In this paper, we introduce a multivariate generalization of the population version of Gini's rank association coefficient, giving a response to this open question posed in [4]. We also study some properties of this version, present the corresponding results for the sample statistic, and provide several examples.     

Acceleration of the Multiple-Try Metropolis algorithm using antithetic and stratified sampling

The Multiple-Try Metropolis is a recent extension of the Metropolis algorithm in which the next state of the chain is selected among a set of proposals. We propose a modification of the Multiple-Try Metropolis algorithm which allows for the use of correlated proposals, particularly antithetic and stratified proposals. The method is particularly useful for random walk Metropolis in high dimensional spaces and can be used easily when the proposal distribution is Gaussian. We explore the use of quasi Monte Carlo (QMC) methods to generate highly stratified samples. A series of examples is presented to evaluate the potential of the method.     

Cox regression analysis in presence of collinearity: an application to assessment of health risks associated with occupational radiation exposure

This paper considers the analysis of time to event data in the presence of collinearity between covariates. In linear and logistic regression models, the ridge regression estimator has been applied as an alternative to the maximum likelihood estimator in the presence of collinearity. The advantage of the ridge regression estimator over the usual maximum likelihood estimator is that the former often has a smaller total mean square error and is thus more precise. In this paper, we generalized this approach for addressing collinearity to the Cox proportional hazards model. Simulation studies were conducted to evaluate the performance of the ridge regression estimator. Our approach was motivated by an occupational radiation study conducted at Oak Ridge National Laboratory to evaluate health risks associated with occupational radiation exposure in which the exposure tends to be correlated with possible confounders such as years of exposure and attained age. We applied the proposed methods to this study to evaluate the association of radiation exposure with all-cause mortality.