Estimating Transition Probabilities from a Time Series of Independent Cross Sections

This paper considers the implementation of a nonstationary, heterogeneous Markov model for the analysis of a binary dependent variable in a time series of independent cross sections. The model, previously considered by M offitt (1993), offers the opportunity to estimate entry and exit transition probabilities and to examine the effects of time-constant and time-varying covariates on the hazards. We show how ML estimates of the parameters can be obtained by Fisher's method-of-scoring and how to estimate both fixed and time-varying covariate effects. The model is exemplified with an analysis of the labor force participation decision of Dutch women using data from the Socio-economic Panel (SEP) study conducted in the Netherlands between 1986 and 1995. We treat the panel data as independent cross sections and compare the employment status sequences predicted by the model with the observed sequences in the panel. Some open problems concerning the application of the model are also discussed.     

On forecasting cointegrated seasonal time series

We analyze periodic and seasonal cointegration models for bivariate quarterly observed time series in an empirical forecasting study. We include both single equation and multiple equation methods for those two classes of models. A VAR model in first differences, with and without cointegration restrictions, and a VAR model in annual differences are also included in the analysis, where they serve as benchmark models. Our empirical results indicate that the VAR model in first differences without cointegration is best if one-step ahead forecasts are considered. For longer forecast horizons however, the VAR model in annual differences is better. When comparing periodic versus seasonal cointegration models, we find that the seasonal cointegration models tend to yield better forecasts. Finally, there is no clear indication that multiple equations methods improve on single equation methods.     

External spillovers, internal spillovers and the geography of production and innovation

We consider a three-location duopoly model such that (i) firms choose production and innovation locations before (Bertrand) competition takes place and (ii) there are internal and external knowledge spillovers. We show: (1) agglomerations where firms earn negative profits may exist when there are both external and internal knowledge spillovers; (2) greater external spillovers do not necessarily favor agglomeration; (3) decreasing communication costs tend to favor agglomeration; (4) there are exactly two types of agglomeration equilibria: either both firms innovate in the agglomeration, or there is an innovator and an imitator; and (5) if there is a location where both firms produce, then innovation must take place in this location.     

Linking job insecurity to well-being and organizational attitudes in Belgian workers: the role of security expectations and fairness

The aim of this study was to investigate how perceptions of job insecurity and fairness associate with individual well-being (job satisfaction and general health) and organizational attitudes (organizational commitment and turnover intention), under the condition that the psychological contract as perceived by the employee includes a promise on job security, or not. More specifically, we suggest that (H1) job insecurity is negatively related to individual well-being and organizational commitment and positively related to turnover intentions when job security is expected as part of the psychological contract, (H2) that job insecurity is negatively related to fairness perceptions when job security is expected as part of the psychological contract, (H3) that fairness associates positively with individual well-being and organizational commitment and negatively with turnover intentions and (H4) that the association between job insecurity, individual well-being and organizational outcomes is mediated by fairness under the condition that job security is expected as part of the psychological contract. Thus, we propose a model of mediated moderation. Results based on a sample of Belgian employees (N = 559) supported our hypotheses for organizational outcomes. For individual well-being, the mediation framework was not conditional upon a perceived promise of job security as part of the psychological contract. We conclude that employees' perceptions of job insecurity and fairness are important factors in employees' well-being and their attitudes towards the organization. More importantly, these perceptions can in part be shaped by organizational agents such as managers and supervisors. Additionally, the active utilization of the psychological contract in management strategies seems to have favourable results for the fostering of pro-organizational attitudes among employees.     

INFORMATION IN THE YIELD CURVE: A MACRO‐FINANCE APPROACH

We use a macro‐finance model, incorporating macroeconomic and financial factors, to study the term premium in the US bond market. Estimating the model using Bayesian techniques, we find that a single factor explains most of the variation in bond risk premiums. Furthermore, the model‐implied risk premiums account for up to 40% of the variability of one‐ and two‐year excess returns. Using the model to decompose yield spreads into an expectations and a term premium component, we find that, although this decomposition does not seem important to forecast economic activity, it is crucial to forecast inflation for most forecasting horizons. Copyright © 2012 John Wiley & Sons, Ltd.     

Managing Risks in Complex Projects

Dealing effectively with risks in complex projects is difficult and requires management interventions that go beyond simple analytical approaches. This is one finding of a major field study into risk management practices and business processes of 35 major product developments in 17 high‐technology companies. Almost one‐half of the contingencies that occur are not being detected before they impact project performance. Yet, the risk‐impact model presented in this article shows that risk does not affect all projects equally but depends on the effectiveness of collective managerial actions dealing with specific contingencies. The results of this study discuss why some organizations are more successful in detecting risks early in the project life cycle, and in decoupling risk factors from work processes before they impact project performance. The field data suggest that effective project risk management involves an intricately linked set of variables, related to work process, organizational environment, and people. Some of the best success scenarios point to the critical importance of recognizing and dealing with risks early in their development. This requires broad involvement and collaboration across all segments of the project team and its environment, and sophisticated methods for assessing feasibilities and usability early and frequently during the project life cycle. Specific managerial actions, organizational conditions, and work processes are suggested for fostering a project environment most conducive to effective cross‐functional communication and collaboration among all stakeholders, a condition important to early risk detection and effective risk management in complex project situations.     

A preference foundation for constant loss aversion

Following prospect theory and in particular the concept of loss aversion, introduced by Kahneman and Tversky (1979), we consider decision making under risk in which the decision maker’s preferences depend on a reference outcome. An outcome below this reference outcome is regarded as resulting from a loss: a loss decreases the decision maker’s basic utility more than a comparable gain increases this utility. An elegant and simple way to model this phenomenon was proposed by Shalev (2002): the utility of an outcome below the reference outcome is obtained from the basic utility by subtracting a multiple of the loss in basic utility: this multiple, the loss aversion coefficient, is constant across different reference outcomes. We provide a preference foundation for this loss aversion model.     

Probability and quantile estimation from individually micro-aggregated data

Micro-aggregation is a frequently used strategy to anonymize data before they are released to the scientific public. A sample of a continuous random variable is individually micro-aggregated by first sorting and grouping the data into groups of equal size and then replacing the values of the variable in each group by their group mean. In a similar way, data with more than one variable can be anonymized by individual micro-aggregation. Data thus distorted may still be used for statistical analysis. We show that if probabilities and quantiles are estimated in the usual way by computing relative frequencies and sample quantiles, respectively, these estimates are consistent and asymptotically normal under mild conditions.