Competitive moment matching of a New-Keynesian and an Old-Keynesian model

The paper considers two rival models referring to the new macroeconomic consensus: a standard three-equation model of the New-Keynesian variety versus dynamic adjustments of a business and an inflation climate in an ‘Old-Keynesian’ tradition. Over the two subperiods of the Great Inflation and Great Moderation, both of them are estimated by the method of simulated moments. An innovative feature is here that the moments do not only include the autocovariances up to eight lags of quarterly output, inflation and the interest rate, but optionally also a measure of the raggedness of the three variables. In short, the performance of the Old-Keynesian model is very satisfactory and similar to the New-Keynesian model, or even better. In particular, the Old-Keynesian model is better suited to match the new moments without deteriorating the original second moments too much.     

Appeals in Korean Magazine Advertising: A Content Analysis and Cross-Cultural Comparison

This paper examines the economic, advertising industry, cultural, and product factors influencing the use of emotional and rational appeals in advertising for South Korea and the U.S. Content analysis of 600 Korean magazine advertisements shows that emotional appeals predominate in headlines, but rational appeals are more common overall and in the illustrations. Compared to results from 403 U.S. advertisements, Korean magazine advertising uses more emotional appeals, though differences vary across product types and executional method. Implications for advertising practice and future research are discussed.     

Applying the method of simulated moments to estimate a small agent-based asset pricing model

The paper takes a recent agent-based asset pricing model by Manzan and Westerhoff from the literature and applies the method of simulated moments to estimate its six parameters. In selecting the moments, the focus is on the fat tails and autocorrelation patterns of the daily returns of several stock market indices and foreign exchange rates. It is argued that it may be meaningful to abandon the econometrically optimal weighting matrix in the objective function and instead invoke the moments' t-statistics in an intuitively appealing way. This modification gives rise to estimations whose moment matching, given the model's parsimony, can be largely considered to be satisfactory. Also the parameter estimates across different markets make good economic sense.     

Effort maximization in asymmetric contest games with heterogeneous contestants

Contest rules are set up by administrators who frequently have discretionary power in specifying the details of these rules, i.e., they can bias the contest rules toward specific contestants in order to further their prime objective. We derive the optimal bias of the contest rule for a contest administrator, who is interested in maximizing the total efforts expended in the contest. The solution is obtained in closed form for a widely used class of n-person contest games. Setting the optimal bias has important implications: (i) there is never exclusion of strong players, instead there is (endogenously induced) inclusion of weak contestants; (ii) the contest administrator will optimally level the playing field by encouraging weak contestants, but he will not equalize the contestants’ chances unless they are identical; and (iii) at least three contestants will be active in equilibrium of the optimal contest, irrespective of heterogeneity.     

Technology for the People? Humanity as a Compass for the Digital Transformation

How do we define what technology is for humans? One perspective suggests that it is a tool enabling the use of valuable resources such as time, food, health and mobility. One could say that in its cultural history, humanity has developed a wide range of artefacts which enable the effective utilisation of these resources for the fulfilment of physiological, but also psychological, needs. This paper explores how this perspective may be used as an orientation for future technological innovation. Hence, the goal is to provide an accessible discussion of such a psychological perspective on technology development that could pave the way towards a truly human-centred digital transformation.

     

Der Relaunch des Journals für Betriebswirtschaft

Ohne Zusammenfassung     

Statistical Inference,Learning and Models in Big Data

The need for new methods to deal with big data is a common theme in most scientific fields, although its definition tends to vary with the context. Statistical ideas are an essential part of this, and as a partial response, a thematic program on statistical inference, learning and models in big data was held in 2015 in Canada, under the general direction of the Canadian Statistical Sciences Institute, with major funding from, and most activities located at, the Fields Institute for Research in Mathematical Sciences. This paper gives an overview of the topics covered, describing challenges and strategies that seem common to many different areas of application and including some examples of applications to make these challenges and strategies more concrete.     

The Social Network Position of Lead Users

The field of lead user research has seen a great deal of attention from academics and practitioners alike. However, we still lack a full understanding of the nature of users with high potential for innovation. In this paper, we employ a social network perspective on lead users. Increasing the realism of our research in three empirical studies with different empirical settings and methods, we provide robust evidence that lead users have a distinctive social network position: They exhibit an unusually high level of “betweenness centrality,” meaning that they are positioned as bridges between different social groups. This finding has two major implications for lead user theory. First, it consolidates seminal conceptual work on lead users and their embeddedness in social networks. And second, the findings extend and validate prior work on the social network perspective of lead users by combining theoretical insights from cognitive psychology, research on creativity, and network theory. As the social network positions of individuals can be mapped quickly and at low cost with modern Web mining tools, our findings may point to a new and readily applicable approach for the efficient and effective identification of lead users in real‐life projects, an aspect that is usually emphasized as the most crucial activity in lead user projects.     

How Fat‐Tailed is US Output Growth?

Several studies have recently rejected the common hypothesis that aggregate output is normally distributed. The present paper reconsiders this issue for US output growth. To this end, it focusses on the shape parameter b of the exponential power distribution (EPD), the two polar values of which constitute the normal distribution and the Laplace distribution with its fatter tails, respectively. The paper first warns against premature conclusions that neglect a structural break in output volatility. On the basis of a battery of Monte Carlo experiments, it is then found out that the results strongly depend on which subperiod is considered, the Great Inflation (GI) or the Great Moderation (GM) period, and which data are referred to, the growth rates of GDP, firm sector output, and quarterly or monthly industrial production (IP). Here, only quarterly IP can be said to exhibit fat tails in GI as well as GM; other evidence is mixed.