Adaptive Multiple Importance Sampling

Abstract. The Adaptive Multiple Importance Sampling algorithm is aimed at an optimal recycling of past simulations in an iterated importance sampling (IS) scheme. The difference with earlier adaptive IS implementations like Population Monte Carlo is that the importance weights of all simulated values, past as well as present, are recomputed at each iteration, following the technique of the deterministic multiple mixture estimator of Owen & Zhou (J. Amer. Statist. Assoc., 95, 2000, 135). Although the convergence properties of the algorithm cannot be investigated, we demonstrate through a challenging banana shape target distribution and a population genetics example that the improvement brought by this technique is substantial.     

Estimating Archimedean Copulas in High Dimensions

Abstract. This article presents a novel estimation procedure for high‐dimensional Archimedean copulas. In contrast to maximum likelihood estimation, the method presented here does not require derivatives of the Archimedean generator. This is computationally advantageous for high‐dimensional Archimedean copulas in which higher‐order derivatives are needed but are often difficult to obtain. Our procedure is based on a parameter‐dependent transformation of the underlying random variables to a one‐dimensional distribution where a minimum‐distance method is applied. We show strong consistency of the resulting minimum‐distance estimators to the case of known margins as well as to the case of unknown margins when pseudo‐observations are used. Moreover, we conduct a simulation comparing the performance of the proposed estimation procedure with the well‐known maximum likelihood approach according to bias and standard deviation.     

ON DISTANCE METRICS IN LOCATION PROBLEMS

Product space metrics reflect the complementarity of product attributes and lead to different geometries in multidimensional spaces. This matters for optimal product positioning. I consider how a planner should design two substitute public goods with multiple attributes to best serve different needs. Attribute complementarity affects whether a given menu dominates another (using an extension of the Pareto criterion). Distancing the goods proportionately in every attribute guarantees improvement when tastes are maximally diverse. The intuition is that the planner must minimize overlap in the populations the goods serve, since benefits are wasted in appealing to the same individual with two offerings. (JEL C65, D78, H41)     

REPUTATION,PRICE, AND DEATH: AN EMPIRICAL ANALYSIS OF ART PRICE FORMATION

We analyze how an artist's death influences the market prices of her works of art. Death has two opposing effects on art prices. By irrevocably restricting the artist's oeuvre, prices, ceteris paribus, increase when the artist dies. On the other hand, an untimely death may well frustrate the collectors' hopes of owning artwork that will, as the artist's career progresses, become generally known and appreciated. By frustrating expected future name recognition, death impacts negatively on art prices. In conjunction, these two channels of influence give rise to a hump‐shaped relationship between age at death and death‐induced price changes. Using transactions from fine art auctions, we show that the empirically identified death effects indeed conform to our theoretical predictions. We derive our results from hedonic art price regressions, making use of a dataset which exceeds the sample size of traditional studies in cultural economics by an order of magnitude. (JEL Z11, J24, G12)     

COMMUNICATION AND INCENTIVE MECHANISMS BASED ON GROUP PERFORMANCE: AN EXPERIMENTAL STUDY OF NONPOINT POLLUTION CONTROL

This article considers whether communication can improve the efficacy of incentive mechanisms designed to correct the problem of moral hazard in groups. In particular, we use experimental economics methods to study environmental targeting instruments proposed by Segerson (1988) for regulating a group of nonpoint source polluters based on ambient concentrations. We find that communication greatly affects group performance, enhancing the efficiency of instruments involving fixed fines but leading to overcompliance, and hence inefficiency, in marginal tax/subsidy instruments. These results have implications for future theoretical development of group performance mechanisms as well as the design of public policies. (JEL H23 , C92 )     

Checking the Grouped Data Version of the Cox Model for Interval-grouped Survival Data

Abstract.  Epidemiology research often entails the analysis of failure times subject to grouping. In large cohorts interval grouping also offers a feasible choice of data reduction to actually facilitate an analysis of the data. Based on an underlying Cox proportional hazards model for the exact failure times one may deduce a grouped data version of this model which may then be used to analyse the data. The model bears a lot of resemblance to a generalized linear model, yet due to the nature of data one also needs to incorporate censoring. In the case of non-trivial censoring this precludes model checking procedures based on ordinary residuals as calculation of these requires knowledge of the censoring distribution. In this paper, we represent interval grouped data in a dynamical way using a counting process approach. This enables us to identify martingale residuals which can be computed without knowledge of the censoring distribution. We use these residuals to construct graphical as well as numerical model checking procedures. An example from epidemiology is provided.     

A WINNING PROPOSITION: THE ECONOMIC IMPACT OF SUCCESSFUL NATIONAL FOOTBALL LEAGUE FRANCHISES

Research has demonstrated that a Super Bowl victory increases the personal income of the individuals in the metropolitan area from which the winning teams come. We argue that the economic benefits should extend beyond just the championship team's city to the cities of teams that experience seasonal success, and thus, the winning percentages of National Football League teams were included in our model. When controlling for sources of bias, winning percentage of the local professional football team had a significant positive effect on real per capita personal income. Explanations for these conclusions are offered from a psychological perspective. ( JEL L83, R19)     

Linear-representation Based Estimation of Stochastic Volatility Models

Abstract.  A new way of estimating stochastic volatility models is developed. The method is based on the existence of autoregressive moving average (ARMA) representations for powers of the log-squared observations. These representations allow to build a criterion obtained by weighting the sums of squared innovations corresponding to the different ARMA models. The estimator obtained by minimizing the criterion with respect to the parameters of interest is shown to be consistent and asymptotically normal. Monte-Carlo experiments illustrate the finite sample properties of the estimator. The method has potential applications to other non-linear time-series models.