Stochastic frontier models with random coefficients

The paper proposes a stochastic frontier model with random coefficients to separate technical inefficiency from technological differences across firms, and free the frontier model from the restrictive assumption that all firms must share exactly the same technological possibilities. Inference procedures for the new model are developed based on Bayesian techniques, and computations are performed using Gibbs sampling with data augmentation to allow finite‐sample inference for underlying parameters and latent efficiencies. An empirical example illustrates the procedure. Copyright © 2002 John Wiley & Sons, Ltd.     

Measuring welfare effects in models with random coefficients

In economic research, it is often important to express the marginal value of a variable in monetary terms. In random coefficient models, this marginal monetary value is the ratio of two random coefficients and is thus random itself. In this paper, we study the distribution of this ratio and particularly the consequences of different distributional assumptions about the coefficients. It is shown that important characteristics of the distribution of the marginal monetary value may be sensitive to the distributional assumptions about the random coefficients. The median, however, is much less sensitive than the mean. Copyright © 2006 John Wiley & Sons, Ltd.     

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.     

Linear prediction and estimation methods for regression models with stationary stochastic coefficients

A linear regression model is proposed in which the coefficient vector is a weakly stationary multivariate stochastic process. The model provides a convinient representation of a general class of nonstationary processes. Prediction and estimation methods are proposed that are linear and relatively easy to compute. The proposed procedures are illustrated by estimation of time-varying GNP multipliers of several macro policy instruments over the period 1891-1970. The results are compatible with theoretical priors and suggest that predictability of policy outcomes depends on the mixture of policy instruments.     

Estimation and testing of Euler equation models with time-varying reduced-form coefficients

This paper studies the estimation and testing of Euler equation models in the framework of the classical two-step minimum-distance method. The time-varying reduced-form model in the first step reflects the adaptation of private agents’ beliefs to the changing economic environment. The presumed ability of Euler conditions to deliver stable parameters indexing tastes and technology is interpreted as a time-invariant second-step model. This paper shows that, complementary to and independent of one another, both standard specification test and stability test are required for the evaluation of an Euler equation. As an empirical application, a widely used investment Euler equation is submitted to examination. The empirical outcomes appear to suggest that the standard investment model has not been a success for aggregate investment.     

On the calculation of the scale elasticity in DEA models

An issue in the multiple-output case within the literature on piecewise linear frontier production functions has been how to determine the nature of the scale properties. It is shown that knowledge of scale efficiency does not permit calculation of scale elasticity or vice versa. For inefficient units there is a relationship between input-saving and output-increasing efficiency measures through the average value of the scale elasticity between the two different reference points on the frontier. A direct approach to determine the scale properties is to calculate the scale elasticity from the dual problem to the calculation of Farrell efficiency measures.     

Semiparametric quantile regression estimation in dynamic models with partially varying coefficients

We study quantile regression estimation for dynamic models with partially varying coefficients so that the values of some coefficients may be functions of informative covariates. Estimation of both parametric and nonparametric functional coefficients are proposed. In particular, we propose a three stage semiparametric procedure. Both consistency and asymptotic normality of the proposed estimators are derived. We demonstrate that the parametric estimators are root-n$n$ consistent and the estimation of the functional coefficients is oracle. In addition, efficiency of parameter estimation is discussed and a simple efficient estimator is proposed. A simple and easily implemented test for the hypothesis of a varying-coefficient is proposed. A Monte Carlo experiment is conducted to evaluate the performance of the proposed estimators.     

Estimation and inference for spatial models with heterogeneous coefficients: An application to US house prices

This paper considers the estimation and inference of spatial panel data models with heterogeneous spatial lag coefficients, with and without weakly exogenous regressors, and subject to heteroskedastic errors. A quasi maximum likelihood (QML) estimation procedure is developed and the conditions for identification of the spatial coefficients are derived. The QML estimators of individual spatial coefficients, as well as their mean group estimators, are shown to be consistent and asymptotically normal. Small‐sample properties of the proposed estimators are investigated by Monte Carlo simulations and results are shown to be in line with the paper's key theoretical findings, even for panels with moderate time dimensions and irrespective of the number of cross‐section units. A detailed empirical application to US house price changes during the 1975–2014 period shows a significant degree of heterogeneity in spatiotemporal dynamics over the 338 Metropolitan Statistical Areas considered.     

Improving the performance of random coefficients demand models: The role of optimal instruments

We shed new light on the performance of Berry, Levinsohn and Pakes’ (1995) GMM estimator of the aggregate random coefficient logit model. Based on an extensive Monte Carlo study, we show that the use of Chamberlain’s (1987) optimal instruments overcomes many problems that have recently been documented with standard, non-optimal instruments. Optimal instruments reduce small sample bias, but they prove even more powerful in increasing the estimator’s efficiency and stability. We consider a wide variety of data-generating processes and an empirical application to the automobile market. We also consider the gains of other recent methodological advances when combined with optimal instruments.     

Relative efficiencies of some simple Bayes estimators of coefficients in dynamic models — I

In this paper we consider some approximations to Bayes estimators of coefficients in simple autoregressive models and give an example of a Monte Carlo experiment where these approximate Bayes estimators yield a substantial improvement over the usual sampling theory or quasi-Bayesian estimators. The practical situation is represented by the case where the coefficient vector is known to lie in or on a hypersphere of radius r with center at 0. We show that arbitrariness in the choice of the value of r is often not catastrophic if r is sufficiently large, but finite.     

The Creation of the Office for National Statistics

A new Office for National Statistics was created in the United Kingdom in 1996 following a series of developments designed to strengthen the relevance and integrity of statistics. This paper describes these fundamental changes and sets out a blueprint for future progress. It also sets current developments in the United Kingdom in an historical and international context.     

浅谈地形测绘技术的自动化

地形测绘是一项综合性的勘测工作,在测量过程中涉及到区域的地形、地貌、地物等各个要素,能从不同方面反映被测量地区的环境状况。随着社会现代化建设步伐的加快,地形测绘在建筑工程、环境工程、电力工程等多个行业中得到了广泛的运用,为工程建设提供了必要的依据。鉴于此,文章主要分析了地形测绘与测绘技术的自动化问题。     

商业银行并购与价值创造分析

本文通过对商业银行并购的价值模型分析,探讨了并购活动对商业银行价值创造的影响,提出了盲目扩大规模的负面效应,从价值创造的视角分析了我国商业银行实施并购中一些值得注意的问题。     

差异化客户细分模型的建立和应用

企业可以从不同的视角对客户进行细分,以前大多数从销售的角度对客户进行细分,难以充分反映客户的价值。本文从客户的价值和客户与企业的战略匹配度两个维度将客户区分为战略客户、利润客户、潜力客户和普通客户,并阐述了模型的应用。     

论价值创造与价值分配的根据

在商品经济条件下,价值是社会产品分配的前提和基础.但是,是否这就意味着价值创造的源泉就是价值分配的依据呢?笔者认为简单地把价值创造的源泉看作价值分配的依据,从而试图从这个角度证明某些社会集团在价值分配上占有更多社会财富的合理性是不正确的.     

臻善新领域创意齐增值--(香港)地铁公司的臻善圈活动

2002—03臻善圈发展方向 使命 培养不断学习、力求创新及臻善的文化,为员工及公司创造价值,令员工倍感自豪。 策略 培育员工以创新思维及有系统的方法来解决问题,并运用数据作为支持。 推广臻善圈活动成为工作的一部分。 扩大各阶层员工参与臻善的人数。 鼓励筹组跨功能/跨企业