The graph of Lindahl correspondence as the unique von Neumann-Morgenstern abstract stable set

In this paper, we consider a continuum economy with a finite number of types of agent, and several private and public goods. The main result of the paper is that the graph of the equal-treatment Lindahl equilibria mapping is the unique abstract stable set with respect to the dominance relation in economies with crowding effects introduced by Vasil'ev et al. in 1995. The external stability of this mapping implies that, for any equal-treatment allocation x in , that is not a Lindahl equilibrium, there exists a subeconomy of such that one of its equal-treatment Lindahl allocations blocks x. This result is a counterpart of the theorem of Mas-Colell for Aumann's atomless market with private goods.     

Job satisfaction and gender: Why are women so happy at work?

By most objective standards, women's jobs are worse than men's, yet women report higher levels of job satisfaction than do men. This paper uses a recent large-scale British survey to document the extent of this gender differential for eight measures of job satisfaction and to evaluate the proposition that identical men and women in identical jobs should be equally satisfied. Neither the different jobs that men and women do, their different work values, nor sample selection account for the gender satisfaction differential. The paper's proposed explanation appeals to the notion of relative well-being, especially relative to workers' expectations. An identical man and woman with the same jobs and expectations would indeed report identical job satisfaction, but women's expectations are argued to be lower than men's. This hypothesis is supported by the finding that the gender satisfaction differential disappears for the young, the higher-educated, professionals and those in male-dominated workplaces, for all of whom there is less likely to be a gender difference in job expectations.     

Proportionally adjusted marginal pricing method to share joint costs

We consider the cost sharing problem with divisible demands of heterogeneous goods. We propose a cost sharing method called Proportionally Adjusted Marginal Pricing (PAMP) method. PAMP is a nonadditive (in the cost function) extension of average cost pricing. We introduce an axiom called Local Independence (LI) and use LI together with Continuity, Proportionality, and Scale Invariance to characterize PAMP. Received: 23 March 2001 / Accepted: 16 November 2001 I thank Yves Sprumont, Hervé Moulin, and Ahmet Alkan for discussions, comments and suggestions. Thanks also go to the associate editor and the referees, whose comments and suggestions greatly improve the paper.     

应该用哪一个模型来描述中国货币市场利率的动态变化

本文论证了双曲模型是描述中国货币市场利率动态变化的最佳单因子利率模型。由极大似然估计可以得到单因子利率模型的边际密度函数。双曲模型的边际密度和非参数估计得到的边际密度函数拟合较好，其表现远远优于几个常见的利率模型（CIR、CKLS和AG模型）。与较一般的Ait-Sahalia模型相比差别很小，但参数形式得到简化，似然比检验也支持这一点。双曲模型在刻画利率的均值回复特征方面还克服了AG模型的不足。     

A two-stage stratified Warner’s randomized response model using optimal allocation

This paper applies Kim and Wardes (2004) stratified Warners randomized response model to Mangat and Singhs (1990) two-stage randomized response model. The proposed stratified randomized response model has an optimal allocation and a large gain in precision. Hence, the estimator based on the proposed method is more efficient than Kim and Wardes (2004) and Mangat and Singhs (1990) estimators under the conditions presented in both the case of completely truthful reporting and that of not completely truthful reporting by the respondents.     

Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models

Nine macroeconomic variables are forecast in a real-time scenario using a variety of flexible specification, fixed specification, linear, and nonlinear econometric models. All models are allowed to evolve through time, and our analysis focuses on model selection and performance. In the context of real-time forecasts, flexible specification models (including linear autoregressive models with exogenous variables and nonlinear artificial neural networks) appear to offer a useful and viable alternative to less flexible fixed specification linear models for a subset of the economic variables which we examine, particularly at forecast horizons greater than 1-step ahead. We speculate that one reason for this result is that the economy is evolving (rather slowly) over time. This feature cannot easily be captured by fixed specification linear models, however, and manifests itself in the form of evolving coefficient estimates. We also provide additional evidence supporting the claim that models which ‘win’ based on one model selection criterion (say a squared error measure) do not necessarily win when an alternative selection criterion is used (say a confusion rate measure), thus highlighting the importance of the particular cost function which is used by forecasters and ‘end-users’ to evaluate their models. A wide variety of different model selection criteria and statistical tests are used to illustrate our findings.     

Introducing Environmental Variables in Nonparametric Frontier Models: a Probabilistic Approach

This paper proposes a general formulation of a nonparametric frontier model introducing external environmental factors that might influence the production process but are neither inputs nor outputs under the control of the producer. A representation is proposed in terms of a probabilistic model which defines the data generating process. Our approach extends the basic ideas from Cazals et al. (2002) to the full multivariate case. We introduce the concepts of conditional efficiency measure and of conditional efficiency measure of order-m. Afterwards we suggest a practical way for computing the nonparametric estimators. Finally, a simple methodology to investigate the influence of these external factors on the production process is proposed. Numerical illustrations through some simulated examples and through a real data set on Mutual Funds show the usefulness of the approach.JEL Classification: C13, C14, D20     

Nonparametric efficiency analysis: A multivariate conditional quantile approach

This paper focuses on nonparametric efficiency analysis based on robust estimation of partial frontiers in a complete multivariate setup (multiple inputs and multiple outputs). It introduces α-quantile efficiency scores. A nonparametric estimator is proposed achieving strong consistency and asymptotic normality. Then if α increases to one as a function of the sample size we recover the properties of the FDH estimator. But our estimator is more robust to the perturbations in data, since it attains a finite gross-error sensitivity. Environmental variables can be introduced to evaluate efficiencies and a consistent estimator is proposed. Numerical examples illustrate the usefulness of the approach.     

Forecasting economic time series with unconditional time-varying variance

The classical forecasting theory of stationary time series exploits the second-order structure (variance, autocovariance, and spectral density) of an observed process in order to construct some prediction intervals. However, some economic time series show a time-varying unconditional second-order structure. This article focuses on a simple and meaningful model allowing this nonstationary behaviour. We show that this model satisfactorily explains the nonstationary behaviour of several economic data sets, among which are the U.S. stock returns and exchange rates. The question of how to forecast these processes is addressed and evaluated on the data sets.