ENVY-FREENESS AND DISTRIBUTIVE JUSTICE

Abstract. The criterion of envy-freeness, according to which no agent should prefer any of his neighbours'allocation to his own, has become a central part of the economic theory of distributive justice. It essentially corresponds to the need to express an ideal of equality in societies where preferences and endowments are heterogeneous. This paper surveys various formulations of the idea of envy-freeness, starting with the simple distribution model, then adding the possibility for agents to have different native talent endowments, and finally moving to models with production. Many classical results are displayed, but emphasis is also put on recent developments, mainly the ideas of 'minimizing envy'and of 'absence of domination'. Five major difficulties facing envyfreeness are identified and listed as directions of future research.     

Market microstructure noise,integrated variance estimators,and the accuracy of asymptotic approximations

A growing literature has been advocating consistent kernel estimation of integrated variance in the presence of financial market microstructure noise. We find that, for realistic sample sizes encountered in practice, the asymptotic results derived for the proposed estimators may provide unsatisfactory representations of their finite sample properties. In addition, the existing asymptotic results might not offer sufficient guidance for practical implementations. We show how to optimize the finite sample properties of kernel-based integrated variance estimators. Empirically, we find that their suboptimal implementation can, in some cases, lead to little or no finite sample gains when compared to the classical realized variance estimator. Significant statistical and economic gains can, however, be recovered by using our proposed finite sample methods.     

An equity characterization of second price auctions when preferences may not be quasilinear

In a single item auction model that allows non-quasilinear preferences, we show that an auction rule satisfies weak envy-freeness for equals, strategy-proofness, and nonimposition if and only if it is a (generalized) second price auction. This characterization is established on a wide class of preference domains, including the quasilinear domain, a positive income effect domain, and a negative income effect domain.     

多变时滞中立型系统稳定性研究

本文研究了一类多时变时滞中立型系统的渐近稳定性问题。基于Lyapunov第二方法,推导出新的时滞依赖稳定性判据。所给得判据可以保证多变时滞中立型系统渐进稳定,结论以线性矩阵不等式(LMI)形式表示,便于应用。仿真结果表明本文稳定性判定准则具有较小的保守性。     

A nonparametric test for equality of distributions with mixed categorical and continuous data

In this paper we consider the problem of testing for equality of two density or two conditional density functions defined over mixed discrete and continuous variables. We smooth both the discrete and continuous variables, with the smoothing parameters chosen via least-squares cross-validation. The test statistics are shown to have (asymptotic) normal null distributions. However, we advocate the use of bootstrap methods in order to better approximate their null distribution in finite-sample settings and we provide asymptotic validity of the proposed bootstrap method. Simulations show that the proposed tests have better power than both conventional frequency-based tests and smoothing tests based on ad hoc smoothing parameter selection, while a demonstrative empirical application to the joint distribution of earnings and educational attainment underscores the utility of the proposed approach in mixed data settings.     

Nonparametric spatial regression under near-epoch dependence

This paper establishes asymptotic normality and uniform consistency with convergence rates of the local linear estimator for spatial near-epoch dependent (NED) processes. The class of the NED spatial processes covers important spatial processes, including nonlinear autoregressive and infinite moving average random fields, which generally do not satisfy mixing conditions. Apart from accommodating a larger class of dependent processes, the proposed asymptotic theory allows for triangular arrays of heterogeneous random fields located on unevenly spaced lattices and sampled over regions of arbitrary configuration. All these features make the results applicable in a wide range of empirical settings.     

A theory of hyperfinite processes: the complete removal of individual uncertainty via exact LLN

The aim of this paper is to provide a viable measure–theoretic framework for the study of random phenomena involving a large number of economic entities. The work is based on the fact that processes which are measurable with respect to hyperfinite Loeb product spaces capture the limiting behaviors of triangular arrays of random variables and thus constitute the `right' class for general stochastic modeling. The primary concern of the paper is to characterize those hyperfinite processes satisfying the exact law of large numbers by using the basic notions of conditional expectation, orthogonality, uncorrelatedness and independence together with some unifying multiplicative properties of random variables. The general structure of the processes is also analyzed via a biorthogonal expansion of the Karhunen–Loéve type and via the representation in terms of the simpler hyperfinite Loeb counting spaces. A universality property for atomless Loeb product spaces is formulated to show the abundance of processes satisfying the law. Generalizations to a hyperfinite number of continuous (or discrete) parameter stochastic processes are considered. The various necessary and sufficient conditions for the validity of the law provide a rather complete understanding about the cancelation of individual risks or uncertainty in general settings. Some explicit asymptotic interpretations are also given.     

Confidence sets for partially identified parameters that satisfy a finite number of moment inequalities

This paper proposes a computationally simple way to construct confidence sets for a parameter of interest in models comprised of moment inequalities. Building on results from the literature on multivariate one-sided tests, I show how to test the hypothesis that any particular parameter value is logically consistent with the maintained moment inequalities. The associated test statistic has an asymptotic chi-bar-square distribution, and can be inverted to construct an asymptotic confidence set for the parameter of interest, even if that parameter is only partially identified. Critical values for the test are easily computed, and a Monte Carlo study demonstrates implementation and finite sample performance.     

Nonparametric/semiparametric estimation and testing of econometric models with data dependent smoothing parameters

We consider nonparametric/semiparametric estimation and testing of econometric models with data dependent smoothing parameters. Most of the existing works on asymptotic distributions of a nonparametric/semiparametric estimator or a test statistic are based on some deterministic smoothing parameters, while in practice it is important to use data-driven methods to select the smoothing parameters. In this paper we give a simple sufficient condition that can be used to establish the first order asymptotic equivalence of a nonparametric estimator or a test statistic with stochastic smoothing parameters to those using deterministic smoothing parameters. We also allow for general weakly dependent data.     

A nonparametric multiple comparison test for differences in scale parameters

A nonparametric multiple comparison test for differences in scale parameters is suggested The asymptotic distribution of the test statistic is derived. A modification of the test when the location parameters are unknown and unequal is suggested. This modified test is not asymptotically distribution free for all underlying location-scale families; however, we give sufficient conditions on the families under which the test is asymptotically distribution free.     

Some identification and estimation results for regression models with stochastically varying coefficients

Although various theoretical and applied papers have appeared in recent years concerned with the estimation and use of regression models with stochastically varying coefficients, little is available in the literature on the properties of the proposed estimators or the identifiability of the parameters of such models. The present paper derives sufficient conditions under which the maximum likelihood estimator is consistent and asymptotically normal and also provides sufficient conditions for the estimation of regression models with stationary stochastically varying coefficients. In many instances these requirements are found to have simple, intuitively appealing interpretations. Consistency and asymptotic normality is also proven for a two-step estimator and a method suggested by Rosenberg for generating initial estimates.     

Estimating semiparametric panel data models by marginal integration

We propose an alternative method for estimating the nonlinear component in semiparametric panel data models. Our method is based on marginal integration that allows us to recover the nonlinear component from an additive regression structure that results from the first differencing transformation. We characterize the asymptotic behavior of our estimator. We also extend the methodology to treat panel data models with two-way effects. Monte Carlo simulations show that our estimator behaves well in finite samples in both random effects and fixed effects settings.     

Some large-concentration-parameter asymptotics for the k-class estimators

A sufficient condition is derived in this paper for the consistency and asymptotic normality of the k-class estimators (k-stochastic or nonstochastic) as the concentration parameter increases indefinitely, with the sample size either staying fixed or also increasing. It is further shown that the limited-information maximum likelihood estimator satisfies this condition. Since large sample size implies a large concentration parameter, but not vice versa, the usual conditions for consistency and asymptotic normality of the k-class estimators as the sample size increases can be inferred from the results given in this paper. But more importantly, the results in this paper shed further light on the small-sample properties of the stochastic k-class estimators and can serve as a starting point for the derivation of asymptotic approximations for these estimators as the concentration parameter goes to infinity, while the sample size either stays fixed or also goes to infinity.     

Micro versus macro cointegration in heterogeneous panels

We consider the issue of cross-sectional aggregation in nonstationary and heterogeneous panels where each unit cointegrates. We derive asymptotic properties of the aggregate estimate, and necessary and sufficient conditions for cointegration to hold in the aggregate relationship. We then analyze the case when cointegration does not carry through the aggregation process, and we investigate whether the violation of the formal conditions for perfect aggregation can still lead to an aggregate equation that is observationally equivalent to a cointegrated relationship. We derive a measure of the degree of noncointegration of the aggregate relationship and we explore its asymptotic properties. We propose a valid bootstrap approximation of the test. A Monte Carlo exercise evaluates size and power properties of the bootstrap test.     

医院推行岗位设置管理应注意的几个问题

岗位设置管理是卫生事业单位人事制度和收入分配制度改革的前提和重要基础。本文从实际出发,阐述了推行岗位设置工作的意义,并主要对当前医院推行岗位设置改革时面临的难点问题进行了分析,并从四个方面有针对性地提出了解决办法。     

The Influence of Environmental Pressures, Diversification Strategy, and Union/Nonunion Setting on Employee Participation

This study tests the hypotheses that environment, diversification strategy, and union/nonunion setting affect the number and variety of employee participation programs. A survey of large U.S. manufacturing firms measured the implementation of employee participation programs. Regression results suggest that environmental pressures exert a direct effect on participation in union settings. However, in nonunion settings, environment and diversification strategy both correlated directly with participation. These results suggest that unions could potentially affect participation program implementation.     

Nonlinear Granger Causality: Guidelines for Multivariate Analysis

We propose an extension of the bivariate nonparametric Diks–Panchenko Granger non‐causality test to multivariate settings. We first show that the asymptotic theory for the bivariate test fails to apply to the multivariate case, because the kernel density estimator bias and variance cannot both tend to zero at a sufficiently fast rate. To overcome this difficulty we propose to reduce the order of the bias by applying data sharpening prior to calculating the test statistic. We derive the asymptotic properties of the ‘sharpened’ test statistic and investigate its performance numerically. We conclude with an empirical application to the US grain market, using the price of futures on heating degree days as an additional conditioning variable. Copyright © 2015 John Wiley & Sons, Ltd.     

Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances

This study develops a methodology of inference for a widely used Cliff–Ord type spatial model containing spatial lags in the dependent variable, exogenous variables, and the disturbance terms, while allowing for unknown heteroskedasticity in the innovations. We first generalize the GMM estimator suggested in  and  for the spatial autoregressive parameter in the disturbance process. We also define IV estimators for the regression parameters of the model and give results concerning the joint asymptotic distribution of those estimators and the GMM estimator. Much of the theory is kept general to cover a wide range of settings.     

Asymptotic stability of a general equilibrium under perfect and monopolistic competition

This study examines the asymptotic stability of a general equilibrium for an economy under perfect and monopolistic competition in which delays in a production process arise. Crucially, we find that the sufficient conditions for the stability of the equilibrium in each model differ markedly. For the stability of the equilibrium under perfect (monopolistic) competition, it is favorable that the slope of every demand curve is gradual (steep).     

A discrete‐choice model for large heterogeneous panels with interactive fixed effects with an application to the determinants of corporate bond issuance

What is the effect of funding costs on the conditional probability of issuing a corporate bond? We study this question in a novel dataset covering 5610 issuances by US firms over the period from 1990 to 2014. Identification of this effect is complicated because of unobserved, common shocks such as the global financial crisis. To account for these shocks, we extend the common correlated effects estimator to settings where outcomes are discrete. Both the asymptotic properties and the small‐sample behavior of this estimator are documented. We find that for non‐financial firms yields are negatively related to bond issuance but that the effect is larger in the pre‐crisis period.