Projection‐Based Inference in Randomised Clinical Trials

Covariate information is often available in randomised clinical trials for each subject prior to treatment assignment and is commonly utilised to make covariate adjustment for baseline characteristics predictive of the outcome in order to increase precision and improve power in the detection of a treatment effect. Motivated by a nonparametric covariance analysis, we study a projection approach to making objective covariate adjustment in randomised clinical trials on the basis of two unbiased estimating functions that decouple the outcome and covariate data. The proposed projection approach extends a weighted least‐squares procedure by projecting one of the estimating functions onto the linear subspace spanned by the other estimating function that is E‐ancillary for the average treatment effect. Compared with the weighted least‐squares method, the projection method allows for objective inference on the average treatment effect by exploiting the treatment specific covariate–outcome associations. The resulting projection‐based estimator of the average treatment effect is asymptotically efficient when the treatment‐specific working regression models are correctly specified and is asymptotically more efficient than other existing competitors when the treatment‐specific working regression models are misspecified. The proposed projection method is illustrated by an analysis of data from an HIV clinical trial. In a simulation study, we show that the proposed projection method compares favourably with its competitors in finite samples.     

Functional Coefficient Cointegration Models Subject to Time–Varying Volatility with an Application to the Purchasing Power Parity

This paper analyses functional coefficient cointegration models with both stationary and non‐stationary covariates, allowing time‐varying (unconditional) volatility of a general form. The conventional kernel weighted least squares (KLS) estimator is subject to potential efficiency loss, and can be improved by an adaptive kernel weighted least squares (AKLS) estimator that adapts to heteroscedasticity of unknown form. The AKLS estimator is shown to be as efficient as the oracle generalized kernel weighted least squares estimator asymptotically, and can achieve significant efficiency gain relative to the KLS estimator in finite samples. An illustrative example is provided by investigating the Purchasing Power Parity hypothesis.     

BALANCED VARIABLE ADDITION IN LINEAR MODELS

This paper studies what happens when we move from a short regression to a long regression in a setting where both regressions are subject to misspecification. In this setup, the least‐squares estimator in the long regression may have larger inconsistency than the least‐squares estimator in the short regression. We provide a simple interpretation for the comparison of the inconsistencies and study under which conditions the additional regressors in the long regression represent a “balanced addition” to the short regression.     

Robust Small Area Estimation under Spatial Non‐stationarity

The effective use of spatial information in a regression‐based approach to small area estimation is an important practical issue. One approach to account for geographic information is by extending the linear mixed model to allow for spatially correlated random area effects. An alternative is to include the spatial information by a non‐parametric mixed models. Another option is geographic weighted regression where the model coefficients vary spatially across the geography of interest. Although these approaches are useful for estimating small area means efficiently under strict parametric assumptions, they can be sensitive to outliers. In this paper, we propose robust extensions of the geographically weighted empirical best linear unbiased predictor. In particular, we introduce robust projective and predictive estimators under spatial non‐stationarity. Mean squared error estimation is performed by two analytic approaches that account for the spatial structure in the data. Model‐based simulations show that the methodology proposed often leads to more efficient estimators. Furthermore, the analytic mean squared error estimators introduced have appealing properties in terms of stability and bias. Finally, we demonstrate in the application that the new methodology is a good choice for producing estimates for average rent prices of apartments in urban planning areas in Berlin.     

Nonparametric identification and estimation of current status data in the presence of death

We present a nonparametric study of current status data in the presence of death. Such data arise from biomedical investigations in which patients are examined for the onset of a certain disease, for example, tumor progression, but may die before the examination. A key difference between such studies on human subjects and the survival–sacrifice model in animal carcinogenicity experiments is that, due to ethical and perhaps technical reasons, deceased human subjects are not examined, so that the information on their disease status is lost. We show that, for current status data with death, only the overall and disease‐free survival functions can be identified, whereas the cumulative incidence of the disease is not identifiable. We describe a fast and stable algorithm to estimate the disease‐free survival function by maximizing a pseudo‐likelihood with plug‐in estimates for the overall survival rates. It is then proved that the global rate of convergence for the nonparametric maximum pseudo‐likelihood estimator is equal to O_p(n^?1/3) or the convergence rate of the estimated overall survival function, whichever is slower. Simulation studies show that the nonparametric maximum pseudo‐likelihood estimators are fairly accurate in small‐ to medium‐sized samples. Real data from breast cancer studies are analyzed as an illustration.     

FDI AND TAXATION: A META‐STUDY

Abstract In this paper we extend former meta‐analyses on FDI and taxation in three ways. First, we add 16 recent publications. Second, we code additional meta‐regressor variables addressing important issues in research on FDI and taxation. Third, we refer to the sophisticated meta‐analytical methodology and present a coherent strategy to choose the meta‐regression estimator most suitable for the meta‐data at hand. As compared to prior surveys, the meta‐analysis is thus based on a much broader methodological basis and a considerably richer meta‐data set. The median tax semi‐elasticity of FDI based on 704 primary estimates is 2.49 in absolute terms. The precision weighted average of the full sample of semi‐elasticities is 2.55, again in absolute terms. Moreover, our meta‐analysis shows that there is a publication bias in the primary literature. Meta‐regressions show that studies based on aggregate data report systematically larger semi‐elasticities than firm‐level analyses, that integrating bilateral tax regulations into effective tax rates leads to more effective measurement of adverse tax incentives on foreign investment, and that tax effects are not compensated by public spending.     

Analysis of binary data with the possibility of wrong ascertainment

Most genetic studies recruit high‐risk families, and the discoveries are based on non‐random selected groups. We must consider the consequences of this ascertainment process to apply the results of genetic research to the general population. In addition, in epidemiological studies, binary responses are often misclassified. We proposed a binary logistic regression model that provides a novel and flexible way to correct for misclassification in binary responses, taking into account the ascertainment issues. A hierarchical Bayesian analysis using Markov chain Monte Carlo method has been carried out to investigate the effect of covariates on disease status. The focus of this paper is to study the effect of classification errors and non‐random ascertainment on the estimates of the model parameters. An extensive simulation study indicated that the proposed model results in substantial improvement of the estimates. Two data sets have been revisited to illustrate the methodology.     

Multi‐state Stochastic Processes: A Statistical Flowgraph Perspective

Two‐state models (working/failed or alive/dead) are widely used in reliability and survival analysis. In contrast, multi‐state stochastic processes provide a richer framework for modeling and analyzing the progression of a process from an initial to a terminal state, allowing incorporation of more details of the process mechanism. We review multi‐state models, focusing on time‐homogeneous semi‐Markov processes (SMPs), and then describe the statistical flowgraph framework, which comprises analysis methods and algorithms for computing quantities of interest such as the distribution of first passage times to a terminal state. These algorithms algebraically combine integral transforms of the waiting time distributions in each state and invert them to get the required results. The estimated transforms may be based on parametric distributions or on empirical distributions of sample transition data, which may be censored. The methods are illustrated with several applications.     

Forecasting With the Standardized Self‐Perturbed Kalman Filter

We propose and study the finite‐sample properties of a modified version of the self‐perturbed Kalman filter of Park and Jun (Electronics Letters 1992; 28 : 558–559) for the online estimation of models subject to parameter instability. The perturbation term in the updating equation of the state covariance matrix is weighted by the estimate of the measurement error variance. This avoids the calibration of a design parameter as the perturbation term is scaled by the amount of uncertainty in the data. It is shown by Monte Carlo simulations that this perturbation method is associated with a good tracking of the dynamics of the parameters compared to other online algorithms and to classical and Bayesian methods. The standardized self‐perturbed Kalman filter is adopted to forecast the equity premium on the S&P 500 index under several model specifications, and determines the extent to which realized variance can be used to predict excess returns. Copyright © 2016 John Wiley & Sons, Ltd.     

Thermal Eco‐cities: Green Building and Urban Thermal Metabolism

Eco‐city projects are becoming increasingly prevalent throughout the globe and are often marketed as ‘new’ urban environments focused on achieving sustainable urban living while promoting environmental–economic transitions towards a low‐carbon technological and industrial base. The article argues for the need to consider the thermal aspects of urban metabolism, while at the same time focusing on the link between individual buildings and eco‐city master plans and wider economic development strategies at a state level. In so doing, the article encourages critical analysis of eco‐city design and planning, while keeping a focus on the role of specific building structures within eco‐cities as examples of the intermeshing of what can be termed a ‘political ecology of scale’ which stretches from specific buildings' climatic characteristics, to the metabolic master plan for eco‐cities, to provincial, regional and state‐level plans for the integration of eco‐cities within wider economic and political development trajectories. The article focuses on Masdar, in Abu Dhabi, an eco‐city under construction at the time of writing.     

Dual careers,time‐use and satisfaction levels: evidence from the British Household Panel Survey

This article empirically examines time‐use and its impact on satisfaction levels among dual career households in a post‐industrial economy, the UK. Analysis explores the 1993–2009 British Household Panel Survey using panel probit regression. The evidence reveals distinctions in time‐use relative to gender, occupations and employment sector. Long hours persist among managers and professionals. The uneven division of household labour, further, continues to burden women with extensive amounts of housework and care. Satisfaction with working hours and amount/use of leisure time are lower among women, especially the public sector professionals. Provision of care, occupation and partner employment characteristics represent important satisfaction determinants present among women, while income (including partner's income) only has relevance among men. Housework does not itself generate dissatisfaction. It is the overload of household tasks, due to inequality in the household division of labour, which constrains many highly skilled working women reducing satisfaction with time‐use and life overall.     

Incentive Effects in Higher Education: an Improved Funding Model for Universities

We examine the incentive effects of risk‐sharing between student and university in the English higher education system. The ‘graduate premium’ has been widely reported and has been used to justify rising higher education participation and increased individual or governmental expenditure. But this premium is simply the mean of a wide distribution, varying, inter alia, by subject, institution, year of graduation and individual. We assume that universities exist in a state of monopolistic competition and are subject to a budget constraint. Using US college data we find evidence suggesting that a funding model which incorporates risk‐sharing improves the efficiency of educational delivery while maintaining subject diversity and access.     

高职物流专业学生顶岗实习的管理

物流是一门系统性、操作性很强的学科,学校必须让学生在较短的时间内掌握必需的理论知识和操作技能。湖北财税职业学院采取了多方位的学生定岗实习的管理措施,学生除在学校学习专业基础理论和技能还要进入企业直接顶岗实习,接受职业培训,提高职业技能。     

Tackling the undeclared economy in the European Union: an evaluation of the tax morale approach

To evaluate a new approach towards tackling the undeclared economy, which views participants as social actors rather than rational economic actors, this article reports evidence from 27,563 face‐to‐face interviews conducted across the European Union during 2013. Multilevel logistic regression analysis reveals a strong association between participation in undeclared work and the level of tax morale. Finding that higher tax morale (and thus a lower propensity to engage in undeclared work) is strongly correlated with greater levels of state intervention but also with individual‐level characteristics such as gender, age, education and employment status, the article concludes not only by confirming a political economy approach and refuting modernization and neo‐liberal explanations and remedies, but also by revealing for the first time the importance of solutions not so far considered, including improving educational attainment, older citizens mentoring for younger people and improving women's participation in the labour force.     

Managerial coaching and subordinates' workplace well‐being: A moderated mediation study

Taking a self‐categorisation perspective, we predict that managerial coaching affects subordinates' workplace well‐being through perceived insider status and that Chinese traditionality moderates this indirect effect. To test these hypotheses, we designed a three‐stage research method to collect data from 276 subordinates in a large state‐owned enterprise located in Shanghai, China. Results indicated that (a) managerial coaching was positively related to subordinates' workplace well‐being; (b) perceived insider status mediated the linkage between managerial coaching and subordinates' workplace well‐being; and (c) Chinese traditionality moderated the indirect relationship between managerial coaching and subordinates' workplace well‐being via perceived insider status, such that the indirect effect was stronger for subordinates with low rather than high Chinese traditionality. This study sheds new light on the intervening process (i.e., perceived insider status) that explains how managerial coaching influences subordinates' workplace well‐being. The findings also extend the current literature by adding a substantive moderator (i.e., Chinese traditionality) to explain when and why subordinates increase their well‐being when faced with managerial coaching.     

Work‐To‐Life Conflict and Enrichment Among International Business Travelers: The Role of International Career Orientation

The present study examines the direct and moderating effects of an international career orientation in the relationship between frequency and duration of international business traveling and work‐to‐life conflict and enrichment. In addition, the effort‐reward imbalance that potentially features in the international business traveler's job was examined in relation to work‐to‐life conflict and enrichment. The study was conducted among 232 Finnish people in jobs requiring international business travel. A moderated hierarchical regression analysis shows that travel of considerable duration and a pronounced effort‐reward imbalance had direct links to work‐to‐life conflict. In contrast, low effort‐reward imbalance and a strong international career orientation had direct links to work‐to‐life enrichment. In addition, having a strong orientation to an international career decreases work‐to‐life conflict when a job requires very frequent traveling, but the moderation effect was not found in relation to duration of traveling. Our findings indicate that interventions aimed at reducing work‐to‐life conflict and increasing work‐to‐life enrichment among international business travelers should focus on fostering a balance between efforts and rewards at work. The extent of employees’ internationalism should also be considered when recruiting people into jobs involving international business travel. © 2014 Wiley Periodicals, Inc.     

Pharmaceutical drug misuse: are industry of employment and occupation risk factors?

We explore the misuse of pharmaceutical drugs in the Australian workforce, focusing on whether any differences exist between workers in particular industries or occupations. In terms of industry, being employed in hospitality is positively associated with pharmaceutical drug misuse, while being employed in finance, insurance and retail is inversely related. In terms of occupation, we find that being a labourer is positively related to misuse of pharmaceutical drugs, while being employed in managerial, professional, sales, clerical or administrative roles is associated with a lower tendency. Further analysis of occupational effects revealed that being in a blue‐collar occupation, as a whole, is positively related to pharmaceutical drug misuse relative to white‐collar employment. Moreover, being employed in higher status roles is associated with a lower likelihood of such behaviour. Our findings imply that particular workplace pressures, cultural norms and/or working conditions might be influential factors behind workers' drug misuse.     

Spline regression for hazard rate estimation when data are censored and measured with error

In this paper, we study an estimation problem where the variables of interest are subject to both right censoring and measurement error. In this context, we propose a nonparametric estimation strategy of the hazard rate, based on a regression contrast minimized in a finite‐dimensional functional space generated by splines bases. We prove a risk bound of the estimator in terms of integrated mean square error and discuss the rate of convergence when the dimension of the projection space is adequately chosen. Then we define a data‐driven criterion of model selection and prove that the resulting estimator performs an adequate compromise. The method is illustrated via simulation experiments that show that the strategy is successful.     

Cointegration and control: Assessing the impact of events using time series data

Control groups can provide counterfactual evidence for assessing the impact of an event or policy change on a target variable. We argue that fitting a multivariate time series model offers potential gains over a direct comparison between the target and a weighted average of controls. More importantly, it highlights the assumptions underlying methods such as difference in differences and synthetic control, suggesting ways to test these assumptions. Gains from simple and transparent time series models are analysed using examples from the literature, including the California smoking law of 1989 and German reunification. We argue that selecting controls using a time series strategy is preferable to existing data‐driven regression methods.     

META‐ANALYSIS OF ECONOMICS RESEARCH REPORTING GUIDELINES

Meta‐regression analysis (MRA) can provide objective and comprehensive summaries of economics research. Their use has grown rapidly over the last few decades. To improve transparency and to raise the quality of MRA, the meta‐analysis of economics research‐network (MAER‐Net) has created the below reporting guidelines. Future meta‐analyses in economics will be expected to follow these guidelines or give valid reasons why a meta‐analysis must deviate from them.