On the Choice of Functional Forms: Summary of a Monte Carlo Experiment

The use of flexible functional forms is a standard practice in applied econometrics. Many flexible forms have been proposed. In this study, we investigate the behavior of three of them—the translog, the symmetric McFadden, and the symmetric generalized Barnett. Based on Monte Carlo experiments, we assess the ability of these forms to test theoretical properties and to measure technological characteristics.     

Little Magazines and Translations

Abstract

Serials and the serials information chain have changed rapidly since the development of the electronic journal. Many recent developments are briefly described, including electronic resource management systems (ERMS) and functional requirements for bibliographic records (FRBR). The constantly changing nature of serials is compared to a psychological midlife crisis.     

环境规制、R&D资本存量与绿色技术创新

科技创新是一个国家经济发展的内生动力。技术创新能力的提升需要持续的R&D投入,而R&D资本存量正是衡量持续研发投入水平的最佳指标。本文基于BEA原理,合理测算出中国大陆30个省区市（西藏除外）的R&D资本存量,并采用省际面板数据,构建空间计量模型,探讨环境规制下R&D资本存量对绿色技术创新的影响机制。研究表明：环境规制与R&D资本存量对绿色技术创新均具有促进作用,但一定强度的环境规制会挤占R&D投入资源,与R&D资本存量的累积发生“负挤出效应”,进而影响绿色技术创新;同时,R&D资本存量、环境规制对中国东部、中部与西部地区的绿色技术创新的影响呈现显著的区域异质性。因此,本文提出了制定差异化的R&D投资和环境规制策略。     

From design to operations: a process management life-cycle performance measurement system for Public-Private Partnerships

Public–Private Partnerships (PPPs) have become a critical vehicle for delivering infrastructure worldwide. Yet, the use of such a procurement strategy has received considerable criticism, as they have been prone to experiencing time/cost overruns and during their operation poorly managed. A key issue contributing to the poor performance of PPPs is the paucity of an effective and comprehensive performance measurement system. There has been a tendency for the performance of PPPs to be measured based on their ex-post criteria of time, cost and quality. Such criteria do not accommodate the complexities and lifecycle of an asset. In addressing this problem, the methodology of sequential triangulation is used to develop and examine the effectiveness of a ‘Process Management Life Cycle Performance Measurement System’. The research provides public authorities and private-sector entities embarking on PPPs with a robust mechanism to effectively measure, control and manage their projects’ life cycle performances, ensuring the assets are ‘future proofed’.     

The Practical Significance of Measurement Error in Pulmonary Function Testing Conducted in Research Settings

Conventional spirometry produces measurement error by using repeatability criteria (RC) to discard acceptable data and terminating tests early when RC are met. These practices also implicitly assume that there is no variation across maneuvers within each test. This has implications for air pollution regulations that rely on pulmonary function tests to determine adverse effects or set standards. We perform a Monte Carlo simulation of 20,902 tests of forced expiratory volume in 1 second (FEV₁), each with eight maneuvers, for an individual with empirically obtained, plausibly normal pulmonary function. Default coefficients of variation for inter‐ and intratest variability (3% and 6%, respectively) are employed. Measurement error is defined as the difference between results from the conventional protocol and an unconstrained, eight‐maneuver alternative. In the default model, average measurement error is shown to be ～5%. The minimum difference necessary for statistical significance at p < 0.05 for a before/after comparison is shown to be 16%. Meanwhile, the U.S. Environmental Protection Agency has deemed single‐digit percentage decrements in FEV₁ sufficient to justify more stringent national ambient air quality standards. Sensitivity analysis reveals that results are insensitive to intertest variability but highly sensitive to intratest variability. Halving the latter to 3% reduces measurement error by 55%. Increasing it to 9% or 12% increases measurement error by 65% or 125%, respectively. Within‐day FEV₁ differences ≤5% among normal subjects are believed to be clinically insignificant. Therefore, many differences reported as statistically significant are likely to be artifactual. Reliable data are needed to estimate intratest variability for the general population, subpopulations of interest, and research samples. Sensitive subpopulations (e.g., chronic obstructive pulmonary disease or COPD patients, asthmatics, children) are likely to have higher intratest variability, making it more difficult to derive valid statistical inferences about differences observed after treatment or exposure.     

A statistical procedure for representing state fragility and transition paths

State fragility is a concept that entered the political discourse in the last decades producing remarkable implications for aid allocation and international policies. The operationalization of this concept has generated a number of composite indices to produce rankings of fragile states. However, the temporal dimension of the driving forces leading to fragility has been rather neglected. This article discusses a statistical procedure that helps to represent the global fragility of a country and the path that a country has followed or will follow in the future when possibly entering into (or escaping from) a fragility condition. Specifically, multiple factor analysis is applied to depict vulnerable and weak countries, and to identify the fundamental forces that determine their overall fragility. Moreover, the trajectories of countries along the years are estimated using partial factor scores. Finally, the path of each country is predicted by means of parsimonious regression models, based on a reduced set of explanatory variables, and according to scenarios elaborated from available international outlooks.     

Estimation of finite population mean in stratified sampling using scrambled responses in the presence of measurement errors

This study focuses on the estimation of population mean of a sensitive variable in stratified random sampling based on randomized response technique (RRT) when the observations are contaminated by measurement errors (ME). A generalized estimator of population mean is proposed by using additively scrambled responses for the sensitive variable. The expressions for the bias and mean square error (MSE) of the proposed estimator are derived. The performance of the proposed estimator is evaluated both theoretically and empirically. Results are also applied to a real data set.     

Estimation of discrete survival function for error‐prone diagnostic tests

The product limit or Kaplan‐Meier (KM) estimator is commonly used to estimate the survival function in the presence of incomplete time to event. Application of this method assumes inherently that the occurrence of an event is known with certainty. However, the clinical diagnosis of an event is often subject to misclassification due to assay error or adjudication error, by which the event is assessed with some uncertainty. In the presence of such errors, the true distribution of the time to first event would not be estimated accurately using the KM method. We develop a method to estimate the true survival distribution by incorporating negative predictive values and positive predictive values, into a KM‐like method of estimation. This allows us to quantify the bias in the KM survival estimates due to the presence of misclassified events in the observed data. We present an unbiased estimator of the true survival function and its variance. Asymptotic properties of the proposed estimators are provided, and these properties are examined through simulations. We demonstrate our methods using data from the Viral Resistance to Antiviral Therapy of Hepatitis C study.     

Oracle GMM estimation for misspecified models via thresholding

We propose a thresholding generalized method of moments (GMM) estimator for misspecified time series moment condition models. This estimator has the following oracle property: its asymptotic behavior is the same as of any efficient GMM estimator obtained under the a priori information that the true model were known. We propose data adaptive selection methods for thresholding parameter using multiple testing procedures. We determine the limiting null distributions of classical parameter tests and show the consistency of the corresponding block-bootstrap tests used in conjunction with thresholding GMM inference. We present the results of a simulation study for a misspecified instrumental variable regression model and for a vector autoregressive model with measurement error. We illustrate an application of the proposed methodology to data analysis of a real-world dataset.     

Time management as a moderator of relations between stressors and employee strain

The purpose of this study was to examine whether time management behaviours moderated relations between stressors (role conflict, role overload, work-family conflict, and family-work conflict) and strain, as measured by scores on the General Health Questionnaire. It was predicted that use of time management behaviours would be negatively associated with strain. These relations, however, were expected to be mediated by feelings of control over time. It was further predicted that the use of time management behaviours would attenuate stressor-strain relations. Data collected from 525 employed men and women indicated, as predicted, that use of time management behaviours was negatively associated with strain. These relations were partially mediated by feelings of control over time. Moderator tests failed to provide support for time management behaviour as a moderator of stressor-strain relations. Implications of findings are discussed and future directions for time management research are suggested.