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.     

Determinants of County-Level Voting Patterns In the 2012 and 2016 Presidential Elections

ABSTRACT

County-level data are used to estimate the incumbent-party share of the two-party vote in the 2012 and the 2016 U.S. Presidential elections. Using a ‘seemingly unrelated estimation’ procedure the regression results for the two elections show that there were some clear differences in the size of marginal effects for several key covariates. For example, income inequality, the size of the black male and black female populations, the size of the Hispanic male population and percent of the population with a college degree all had significantly larger coefficients in 2016 than in 2012, producing a larger marginal effect in favour of the Democratic candidate’s vote share. On the other hand, counties with increased poverty rates and counties located on the periphery of urban centres had a significantly larger marginal effect favouring the Republican’s vote share in 2016 compared to 2012. Finally, the regression results show that the effects of third-party vote shares, though not statistically different across the two elections, had a positive impact on the Democratic vote share in both elections.     

Asymptotic expansion of S-estimators of location and covariance

By means of a straightforward application of empirical process theory, we show that S-estimators of multivariate location and covariance are asymptotically equivalent to a sum of independent vector and matrix valued random elements respectively. This provides an alternative proof of asymptotic normality of S-estimators and clearly explains the limiting covariance structure. It also leads to a relatively simple proof of asymptotic normality of the length of the shortest α-fraction.     

Estimation of the scale matrix of a multivariate t-model under entropy loss

This paper deals with the estimation of the scale matrix of a multivariatet-model with unknown location vector and scale matrix to improve upon the usual estimators based on the sample sum of product matrix. The well-known results of the estimation of the scale matrix of the multivariate normal model under the assumption of entropy loss function have been generalized to that of a multivariatet-model. The paper is based on the first author’s unpublished Ph.D. dissertation ‘Estimation of the Scale Matrix of a Multivariate T-model’, University of Western Ontario, Canada. Present address: School of Mathematics and Statistics, The University of Sydney, NSW 2006, Australia.     

On estimating the reliability after sequentially estimating the mean: The exponential case

Sequential estimation problems for the mean parameter of an exponential distribution has received much attention over the years. Purely sequential and accelerated sequential estimators and their asymptotic second-order characteristics have been laid out in the existing literature, both for minimum risk point as well as bounded length confidence interval estimation of the mean parameter. Having obtained a data set from such sequentially designed experiments, the paper investigates estimation problems for the associatedreliability function. Second-order approximations are provided for the bias and mean squared error of the proposed estimator of the reliability function, first under a general setup. An ad hoc bias-corrected version is also introduced. Then, the proposed estimator is investigated further under some specific sequential sampling strategies, already available in the literature. In the end, simulation results are presented for comparing the proposed estimators of the reliability function for moderate sample sizes and various sequential sampling strategies.     

Nonlinearities in the real exchange rates: new evidence from developed and developing countries

This paper investigates nonlinearities in the dynamics of real exchange rates. We use Monte Carlo simulations to establish the size properties of the Teräsvirta-Anderson test, when the dynamics of the real exchange rate is influenced by an exogenous process. In addition, we show that a modified nonlinearity test, which includes additional right-hand-side variables, performs much better than the original in both Monte Carlo exercises and in the actual data on 1431 bilateral real exchange rate series. Finally, we investigate the dynamics of the real exchange rate for both developed and developing countries using the modified test for the recent floating period. In general, the results find a greater incidence of nonlinear dynamics for developing country real exchange rates.     

Nonlinearity in dynamic adjustment: Semiparametric estimation of panel labor supply

We estimate a semiparametric dynamic panel data model by the local linear kernel method and we interpret the slope of the nonparametric component function as a varying slope coefficient. Thus, the slope coefficient is a smooth, but otherwise unknown, function of some of the regressors. A Monte Carlo experiment is reported to examine the finite sample performance of the local linear estimator. We apply the estimation method to a labor supply equation for men from the triannual Survey of Income and Program Participation (SIPP). Specification tests based on the estimated labor supply elasticities, partial adjustment coefficients, and residuals demonstrate the improvements from a semiparametric partially linear model. Our empirical results point to a need by economists to revisit the issue of the speed of labor market adjustment to policy induced shifts in labor demand and to take more formal econometric account of heterogeneity in wage effects when studying the distributional consequences of tax reforms for labor supply earnings. First version received: July 2000/Final version received: January 2001     

Recovering Hicksian Consumer Surplus within a Collective Model: Hausman's Method for the Household

The purpose of this paper is to describe the implications of the collective model of household behavior for the methods used to estimate the economic value of non-marketed environmental resources. After demonstrating how the separability restrictions inherent in the collective model allow individual preference and household income allocation choices to be distinguished, the paper demonstrates how the framework can be used to recover Hicksian consumer surplus. An algebraic example is used to illustrate how the framework can be used in valuing environmental resources.     

基于生态-技术创新和熵理论的企业绿色创新风险度量研究

历史的考察表明,企业进程的实然状态是非生态化的,而生态觉醒之后的企业发展的应然趋势是生态化,导致二者背离的恰恰是作为推动企业发展的＂技术创新→利润＂中轴。随着绿色消费在全球的兴起,绿色企业将成为未来的新型主导企业。文章基于可持续发展的视角,将生态理论和熵理论引入技术创新风险管理领域,建立了基于生态-技术创新和熵理论的技术创新风险度量模型,并将该模型用于企业对技术创新项目进行风险度量和决策。