Slack in incomplete markets with nominal assets: A symmetric proof

In this paper, we provide an equilibrium analysis in the framework of incomplete markets where some agents’ preferences are possibly satiated at some state of the nature. We will consider nominal assets with exogenously fixed asset prices. We extend the notion of equilibrium with slack – introduced by Drèze and Müller [Drèze, J., Müller, H., 1980. Optimality properties of rationing schemes. Journal of Economic Theory 23, 150–159] in a fixed price setting – to the GEI framework.     

Nonparametric density estimation for symmetric distributions by contaminated data

A semiparametric two-component mixture model is considered, in which the distribution of one (primary) component is unknown and assumed symmetric. The distribution of the other component (admixture) is known. We consider three estimates for the pdf of primary component: a naive one, a symmetrized naive estimate and a symmetrized estimate with adaptive weights. Asymptotic behavior and small sample performance of the estimates are investigated. Some rules of thumb for bandwidth selection are discussed.     

Path analysis with partial association measures

This paper discusses the use of partial association measures for carrying out path analyses on categorical data. The measures considered are essentially PRE (proportion of reduction in error of prediction) measures for nominal variables and concordance-discordance indices for ordinal ones. These measures provide a natural way to evaluate the strength of the path linking a non-measurable response variable to one of several categorical explanatory factors. Concerning the decomposition of raw association into direct and indirect effects, it is shown, however, that they do not share the properties of conventional path coefficients for measurable variables. Especially purely nominal association measures need to be interpreted with care. The scope of the partial measures for path analysis is illustrated through a study of the relationships between the educational styles experienced by swiss adolescents and their selfesteem.     

Reverse efficiency measures for environmental assessment in data envelopment analysis

Environmental issues are becoming more and more important in our everyday life. Data Envelopment Analysis (DEA) is a tool developed for measuring relative operational efficiency. DEA can also be employed to estimate environmental efficiency where undesirable outputs like greenhouse gases exist. The classical DEA method identifies best practices among a given empirical data set. In many situations, however, it is advantageous to determine the worst practices and perform efficiency evaluation by comparing DMUs with the full-inefficient frontier. This strategy requires that the conventional production possibility set is defined from a reverse perspective. In this paper, presence of both desirable and undesirable outputs is assumed and a methodological framework for performing an unbiased efficiency analysis is proposed. The reverse production possibility set is defined and new models are presented regarding the full-inefficient frontier. The operational, environmental and overall reverse efficiencies are studied. The important notion of weak disposability is discussed and the effects of this assumption on the proposed models are investigated. The capability of the proposed method is examined using data from a real-world application about paper production.     

Criteria for Justice

A bstract . Six different but common criteria for describing the meaning of distributive justice are examined. The six are (1) to each the same thing; (2) to each according to his or her merit or achievement; (3) to each according to his or her work or contribution; (4) to each according to his or her need or requirements; (5) to each according to his or her status or position; and (6) to each according to his or her contract or agreements. An attempt is made to show how these six can be viewed compatibly and how all support the right of equal access to land and the duty to pay to the community which created it the full economic rent on the site .     

Real and nominal interest rates and the demand for housing

This paper is an empirical study of the relationship between inflation expectations and the demand for housing. The major findings are that housing demand is a function of both expected inflation and the real interest rate but that demand cannot be specified as a function of the nominal rate. It is also shown that expected inflation has a larger impact on those households that are likely to be constrained by capital market imperfections.     

Least squares fitting of an affine function and strength of association for interval-valued data

The ultimate goal of this paper is to determine a measure of the degree of dependence between two interval-valued random sets, when the dependence is intended in the sense of an affine function relating these random elements. For this purpose, a general study on the least squares fitting of an affine function for interval-valued data is first carried out, where the least squares method we will present considers that squared residuals are based on a generalized metric on the space of nonempty compact intervals, and output and input random mechanisms are modelled by means of convex compact random sets. For the general case of nondegenerate convex compact random sets, solutions are presented in an algorithmic way, and the few cases leading to nonunique solutions are characterized. On the basis of this regression study we later introduce and analyze a well-defined determination coefficient of two interval-valued random sets, which will allow us to quantify the strength of association between them, and an algorithm for the computation of the coefficient has been also designed. Finally, a real-life example illustrates the study developed in the paper.  The research in this paper has been partially supported by DGESIC Grants DGE-99-PB98-1534 and DGE-98-PB97-1282 of the Spanish MEC. Their financial support is gratefully acknowledged. Acknowledgements. The authors are deeply grateful to Professor Wolfgang N?ther from the University of Freiberg and their colleagues Professors Norberto Corral, Miguel López-Dı′az and Ignacio MartVnez for their valuable comments and suggestions in connection with this paper. The real-life data which illustrate the study in this paper have been supplied by Aurora Fonseca, Head of the Servicio de Nefrologı′a, and Miguel de Zárraga, General Medical Manager, from the Hospital Valle del Nalón de Langreo in Asturias; we want to thank them sincerely for their collaboration and support.     

On the desirability of nominal GDP targeting

This paper evaluates the welfare properties of nominal GDP targeting in the context of a New Keynesian model with both price and wage rigidity. In particular, we compare nominal GDP targeting to inflation and output gap targeting as well as to a conventional Taylor rule. These comparisons are made on the basis of welfare losses relative to a hypothetical equilibrium with flexible prices and wages. Output gap targeting is the most desirable of the rules under consideration, but nominal GDP targeting performs almost as well. Nominal GDP targeting is associated with smaller welfare losses than a Taylor rule and significantly outperforms inflation targeting. Relative to inflation targeting and a Taylor rule, nominal GDP targeting performs best conditional on supply shocks and when wages are sticky relative to prices. Nominal GDP targeting may outperform output gap targeting if the gap is observed with noise, and has more desirable properties related to equilibrium determinacy than does gap targeting.     

Criteria for classifying forecasting methods

Classifying forecasting methods as being either of a “machine learning” or “statistical” nature has become commonplace in parts of the forecasting literature and community, as exemplified by the M4 competition and the conclusion drawn by the organizers. We argue that this distinction does not stem from fundamental differences in the methods assigned to either class. Instead, this distinction is probably of a tribal nature, which limits the insights into the appropriateness and effectiveness of different forecasting methods. We provide alternative characteristics of forecasting methods which, in our view, allow to draw meaningful conclusions. Further, we discuss areas of forecasting which could benefit most from cross-pollination between the ML and the statistics communities.     

品牌形象代言人的选择标准

通过代言人传播信息是现代传播的重要特点.现在,形象代言人在许多传播领域被广泛使用,如国家旅游业的形象代言人、企业形象代言人、事业单位形象代言人、行业形象代言人、某项活动形象代言人、产品形象代言人等等.但是,怎样寻找到适合自己企业的品牌形象代言人,却不是一个简单地找"名人"的问题.     

The effects of sampling on measures of association between variables based on sample means

Many studies that involve people's perceptions or behaviors focus on aggregate rather than individual responses. For example, variables describing public perceptions for some set of events may be represented as mean scores for each event. Event mean scores then become the unit of analysis for each variable. The variance of these mean scores for a variable is not only a function of the variation among the events themselves, but is also due to the variation among respondents and their possible responses. This is also the case for the covariances between variables based on event mean scores. In many contexts the variance and covariance components attributable to the sampling of respondents and their responses may be large; these components can be described as measurement error. In this paper we show how to estimate variances and covariances of aggregate variables that are free of these sources of measurement error. We also present a measure of reliability for the event means and examine the effect of the number of respondents on these spurious components. To illustrate how these estimates are computed, forty-two respondents were asked to rate forty events on seven risk perception variables. Computing the variances and covariances for these variables based on event means resulted in relatively large components attributable to measurement error. A demonstration is given of how this error is removed and the resulting effect on our estimates.     

Modeling microstructure price dynamics with symmetric Hawkes and diffusion model using ultra-high-frequency stock data

This study examines the theoretical and empirical perspectives of the symmetric Hawkes model of the price tick structure. Combined with the maximum likelihood estimation, the model provides a proper method of volatility estimation specialized in ultra-high-frequency analysis. Empirical studies based on the model using the ultra-high-frequency data of stocks in the S&P 500 are performed. The performance of the volatility measure, intraday estimation, and the dynamics of the parameters are discussed. A new approach of diffusion analogy to the symmetric Hawkes model is proposed with the distributional properties very close to the Hawkes model. As a diffusion process, the model provides more analytical simplicity when computing the variance formula, incorporating skewness and examining the probabilistic property. An estimation of the diffusion model is performed using the simulated maximum likelihood method and shows similar patterns to the Hawkes model.     

Efficiency aggregation with enhanced Russell measures in data envelopment analysis

In aggregation for data envelopment analysis (DEA), a jointly determined aggregate measure of output and input efficiency is desired that is consistent with the individual decision making unit measures. An impasse has been reached in the current state of the literature, however, where only separate measures of input and output efficiency have resulted from attempts to aggregate technical efficiency with the radial measure models commonly employed in DEA. The latter measures are “incomplete” in that they omit the non-zero input and output slacks, and thus fail to account for all inefficiencies that the model can identify. The Russell measure eliminates the latter deficiency but is difficult to solve in standard formulations. A new approach has become available, however, which utilizes a ratio measure in place of the standard formulations. Referred to as an enhanced Russell graph measure (ERM), the resulting model is in the form of a fractional program. Hence, it can be transformed into an ordinary linear programming structure that can generate an optimal solution for the corresponding ERM model. As shown in this paper, an aggregate ERM can then be formed with all the properties considered to be desirable in an aggregate measure—including jointly determined input and output efficiency measures that represent separate estimates of input and output efficiency. Much of this paper is concerned with technical efficiency in both individual and system-wide efficiency measures. Weighting systems are introduced that extend to efficiency-based measures of cost, revenue, and profit, as well as derivatives such as rates of return over cost. The penultimate section shows how the solution to one model also generates optimal solutions to models with other objectives that include rates of return over cost and total profit. This is accomplished in the form of efficiency-adjusted versions of these commonly used measures of performance.     

Technical efficiency measures for dairy farms using panel data: A comparison of alternative model specifications

This article examines the impact of fixed effects production functions vis-à-vis stochastic production frontiers on technical efficiency measures. An unbalanced panel consisting of 96 Vermont dairy farmers for the 1971–1984 period was used in the analysis. The models examined incorporated both time-variant and time-invariant technical efficiency. The major source of variation in efficiency levels across models stemmed from the assumption made concerning the distribution of the one-sided term in the stochastic frontiers. In general, the fixed effects technique was found superior to the stochastic production frontier methodology. Despite the fact that the results of various statistical tests revealed the superiority of some specifications over others, the overall conclusion of the study is that the efficiency analysis was fairly consistent throughout all the models considered.