How Journalists Think about Media Effects—And Why We Should Care

Research suggests that journalists’ beliefs about media effects are influenced by unsystematically gathered knowledge and subjective-intuitive judgments. However, it has also been shown that these presumptions must be considered important factors for the formation of journalistic coverage. Against this background, this article synthesizes existing research on dimensions, determinants, and consequences of journalists’ presumptions of media effects. The resulting framework offers researchers in the field of journalistic content production a comprehensive overview of the possible role that presumptions of media effects could play for journalistic content creation. In a second step, we summarize the implications that the current state of research points at. We discuss why journalism scholars should integrate presumed media effects into their research agendas and what communication researchers, as well as journalists themselves, could do to promote more realistic beliefs about media effects among journalists.     

Inventory leanness and the financial performance of firms

This paper examines the financial consequences that inventory leanness has on firm performance. We conduct an econometric analysis using 4324 publicly traded US manufacturing companies for the period 1980–2008. Using an instrumental variable fixed effects estimator we find a nonlinear relationship between inventory leanness and financial performance. However, we note that the maximum point of this inverted U-shaped relationship often lies at the extreme end of the investigated sample – suggesting a decreasing return from leanness rather than an optimal level. We show that the strength of this relationship is highly dependent on both the industry and inventory component (raw materials, work in process and finished goods) studied. The main novelty and direct implication of our findings is that most firms still have much potential to increase profitability by becoming leaner and they are unlikely to cross a threshold where profitability decreases with increased leanness. We display how much the average firm could gain by becoming leaner and show how this sensitivity changes by inventory component and industry. Finally, we highlight several new econometric aspects that we believe must be addressed when empirically investigating the inventory-performance link.     

Size and power of some cointegration tests under structural breaks and heteroskedastic noise

In this paper we examine the power and size distortions of a number of representative cointegration tests in some large-scale Monte Carlo simulations, when the underlying system is subjected to regime shifts and conditional (ARCH-type) heteroskedasticity. Following the suggestion by Gregory and Hansen, we select the minimum (maximum for the Johansen test) value for the statistics evaluated over a set of tentative break points for the regime shifts. The performance of these statistics is compared to the corresponding ordinary statistics in the presence of regime shifts. We demonstrate that the size of the cointegration tests is severely distorted by conditional heteroskedasticity.     

Using quantile regression for duration analysis

Summary Quantile regression methods are emerging as a popular technique in econometrics and biometrics for exploring the distribution of duration data. This paper discusses quantile regression for duration analysis allowing for a flexible specification of the functional relationship and of the error distribution. Censored quantile regression addresses the issue of right censoring of the response variable which is common in duration analysis. We compare quantile regression to standard duration models. Quantile regression does not impose a proportional effect of the covariates on the hazard over the duration time. However, the method cannot take account of time-varying covariates and it has not been extended so far to allow for unobserved heterogeneity and competing risks. We also discuss how hazard rates can be estimated using quantile regression methods. This paper benefitted from the helpful comments by an anonymous referee. Due to space constraints, we had to omit the details of the empirical application. These can be found in the long version of this paper, Fitzenberger and Wilke (2005). We gratefully acknowledge financial support by the German Research Foundation (DFG) through the research project ‘Microeconometric modelling of unemployment durations under consideration of the macroeconomic situation’. Thanks are due to Xuan Zhang for excellent research assistance. All errors are our sole responsibility.     

Estimating the reliability of repeatedly measured endpoints based on linear mixed‐effects models. A tutorial

There are various settings in which researchers are interested in the assessment of the correlation between repeated measurements that are taken within the same subject (i.e., reliability). For example, the same rating scale may be used to assess the symptom severity of the same patients by multiple physicians, or the same outcome may be measured repeatedly over time in the same patients. Reliability can be estimated in various ways, for example, using the classical Pearson correlation or the intra‐class correlation in clustered data. However, contemporary data often have a complex structure that goes well beyond the restrictive assumptions that are needed with the more conventional methods to estimate reliability. In the current paper, we propose a general and flexible modeling approach that allows for the derivation of reliability estimates, standard errors, and confidence intervals – appropriately taking hierarchies and covariates in the data into account. Our methodology is developed for continuous outcomes together with covariates of an arbitrary type. The methodology is illustrated in a case study, and a Web Appendix is provided which details the computations using the R package CorrMixed and the SAS software. Copyright © 2016 John Wiley & Sons, Ltd.