Seniority bias in a tournament

The paper presents a model where the probability of promotion tends to increase with seniority (overall labor market experience) without relying on the accumulation of general human capital. To this end, we consider the optimal design of a tournament (a relative compensation scheme) between two agents with different time horizon, the young and the old, in an overlapping generations framework. When the principal can only imperfectly monitor each agent’s effort level, the difference in time horizon leads to the ex post difference in the marginal value of effort between the two agents. In this case, the optimal tournament necessarily involves a bias towards the old agent. Within this framework, we also examine the relationship between: (1) the monitoring accuracy and the optimal bias; and (2) the value of outside options and the optimal bias.     

Hierarchical Models for the Analysis of Likert Scales in Regression and Item Response Analysis

Appropriate modelling of Likert‐type items should account for the scale level and the specific role of the neutral middle category, which is present in most Likert‐type items that are in common use. Powerful hierarchical models that account for both aspects are proposed. To avoid biased estimates, the models separate the neutral category when modelling the effects of explanatory variables on the outcome. The main model that is propagated uses binary response models as building blocks in a hierarchical way. It has the advantage that it can be easily extended to include response style effects and non‐linear smooth effects of explanatory variables. By simple transformation of the data, available software for binary response variables can be used to fit the model. The proposed hierarchical model can be used to investigate the effects of covariates on single Likert‐type items and also for the analysis of a combination of items. For both cases, estimation tools are provided. The usefulness of the approach is illustrated by applying the methodology to a large data set.     

Dynamic business modeling for sustainability: Exploring a system dynamics perspective to develop sustainable business models

In the last decade, business models for sustainability have gained increasing attraction by corporate sustainability scholars with international conferences and scientific journals encouraging the development of the debate on their design, use and innovation processes. Capitalizing on the basic principles, requirements, and methodological limitations found in the literature on sustainability‐oriented business model design, this paper aims to conceptualize a dynamic business modeling for sustainability approach, which combines an adapted sustainable business model canvas and system dynamics modeling. To this end, the paper also illustrates the key operating principles of the proposed approach through an exemplary application to Patagonia's business model. Findings suggest that dynamic business modeling for sustainability may contribute to sustainable business model research and practice by introducing a systemic design tool, which frames environmental, social, and economic drivers of value generation into a dynamic business model causal feedback structure, thus overcoming methodological gaps of the extant business model design tools.     

Statistica data analytica est et aliter*

This paper comprises general comments on some current statistical issues, inspired in part by some papers that have been published in Statistica Neerlandica. Occasional glimpses of the future are hazarded.     

Quality control for structural credit risk models

Over the last four decades, a large number of structural models have been developed to estimate and price credit risk. The focus of the paper is on a neglected issue pertaining to fundamental shifts in the structural parameters governing default. We propose formal quality control procedures that allow risk managers to monitor fundamental shifts in the structural parameters of credit risk models. The procedures are sequential — hence apply in real time. The basic ingredients are the key processes used in credit risk analysis, such as most prominently the Merton distance to default process as well as financial returns. Moreover, while we propose different monitoring processes, we also show that one particular process is optimal in terms of minimal detection time of a break in the drift process and relates to the Radon–Nikodym derivative for a change of measure.     

Machine learning advances for time series forecasting

In this paper, we survey the most recent advances in supervised machine learning (ML) and high-dimensional models for time-series forecasting. We consider both linear and nonlinear alternatives. Among the linear methods, we pay special attention to penalized regressions and ensemble of models. The nonlinear methods considered in the paper include shallow and deep neural networks, in their feedforward and recurrent versions, and tree-based methods, such as random forests and boosted trees. We also consider ensemble and hybrid models by combining ingredients from different alternatives. Tests for superior predictive ability are briefly reviewed. Finally, we discuss application of ML in economics and finance and provide an illustration with high-frequency financial data.     

THE ESTIMATION OF HUMAN CAPITAL BY ADMINISTRATIVE ARCHIVES IN A STATIC AND LONGITUDINAL PERSPECTIVE: THE CASE OF MILAN

Abstract This paper focuses on the estimation of the latent variable human capital (HC) at disaggregated level (worker) by available routinely institutional data flows. In particular we utilize the Lombardy region administrative archive ‘Employment Centers of the Province of Milan’, collecting information about careers of workers in the private sector of the Milan area, and administrative flows collecting mandatory workers' individual income tax returns, filed with the National Internal Revenue Service. First, we propose and empirically estimate HC scores in a static (referred to 2004) framework, by means of a realistic measurement model within causal relationships among endogenous and exogenous (investment) HC indicators. Furthermore, the model also specifies a set of (concomitant) indicators that, not belonging to HC investment indicators, have causal impact on endogenous variables and on HC scores, too. Second, we propose a longitudinal analysis (period 2000–2004) aimed to investigate how workers' earned income growth rates vary over workers' educational levels and other personal characteristics. The empirical results of both analyses confirm the characteristics of the Italian job market, denoted by marked inequalities, and knowledge regarding the process of school to work transition, characterized by a weak incidence of education on longitudinal trajectories of earned income.     

会计管理中的风险控制

会计管理的本质是控制,有效地控制风险是现代会计管理的核心内容。要实现会计管理的风险控制功能,就必须了解现代企业风险的模式,认识企业经营风险的基本特征,建立会计管理与风险控制相结合的制度。     

Using data envelopment analysis to measure the efficiency of non-profit organizations: A critical evaluation

This study examines the potential effects of variable set expansion and data variations upon the efficiency scores generated using the Data Envelopment Analysis (DEA) model. It was found that variable set expansion (either through disaggregation of existing variables or addition of new factors) should produce an upward trend in efficiency scores. In addition, ample opportunity exists for ‘decision-making units’ to increase their efficiency scores through manipulation of reported data. In real-world applications of DEA, these problems must be resolved as much as possible (e.g. increased audit of data) in order to improve DEA's practical usefulness and reliability.     

ECONOMETRIC APPROACHES TO EMPIRICAL MODELS OF EXCHANGE RATE DETERMINATION

Abstract. This paper identifies four principal econometric approaches to the estimation and testing of asset market models of exchange rate determination: the traditional, static reduced-form approach; the error correction and co-integration, dynamic reduced-form approaches; the simultaneous equations approach; and large scale, multi-equation macroeconometric simulation models. Each of these econometric approaches is evaluated with respect to its theoretical validity and the comparative properties of the empirical results obtained. This leads to the conclusion that although there may be little to choose between the different theoretical exchange rate models, there may be grounds for favouring a multi-equation, simultaneous estimation procedure for this class of models.