Advertising and search engines. A model of leadership in search advertising

We analyze the role of leadership in a multi-sided market as search advertising, assuming quantity competition and different entry conditions (with barriers to entry or endogenous entry). The model can be microfounded taking into account network effects, multi-homing on the advertising side and scale in search. If there are barriers to entry and the network effects are strong, there is an incentive for the leader to exploit them and attract more consumers to monopolize advertising. Under barriers to entry, the leading platform has also a strategic incentive to exploit scale in search, to manipulate search results to divert search traffic from other platforms, and to introduce limits to multi-homing, with the aim of expanding its market share and deny scale to competitors.     

Robust tests for time-invariant individual heterogeneity versus dynamic state dependence

We derive tests for persistent effects in a general linear dynamic panel data context. Two sources of persistent behavior are considered: time-invariant unobserved factors (captured by an individual random effect) and dynamic persistence or “state dependence” (captured by autoregressive behavior). We will use a maximum likelihood framework to derive a family of tests that help researchers learn whether persistence is due to individual heterogeneity, dynamic effect, or both. The proposed tests have power only in the direction they are designed to perform, that is, they are locally robust to the presence of alternative sources of persistence, and consequently, are able to identify which source of persistence is active. A Monte Carlo experiment is implemented to explore the finite sample performance of the proposed procedures. The tests are applied to a panel data series of real GDP growth for the period 1960–2005.     

Supply chain configurations in a global environment: A longitudinal perspective

In the last few years, companies have paid growing attention to the management of their supply chain at a global level. The need for better suppliers, international competition and research of specific competences have forced companies to improve their ability to cope with suppliers and customers located in different countries around the world. This paper aims to provide an overview of how manufacturing companies use global supply chains and how their behaviour changes over time. Longitudinal data from a sample of companies from the last two editions of the International Manufacturing Strategy Survey (IMSS) are used. A contingent analysis of manufacturing localization and globalization is also considered in order to identify factors influencing supply chain globalization strategies. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.

Endogenous market structures and the optimal financial structure

Abstract We characterize the optimal financial structure as a strategic device to optimize the value of a firm competing in a market where entry is endogenous. Debt financing is always optimal under quantity competition, and, contrary to the Brander‐Lewis‐Showalter results based on duopolies, we show the optimality of moderate debt financing also under price competition with cost uncertainty (but not with demand uncertainty). We derive the formulas for the optimal financial structure, which does not affect the strategies of the other firms but reduces their number.     

Beta-product dependent Pitman–Yor processes for Bayesian inference

Multiple time series data may exhibit clustering over time and the clustering effect may change across different series. This paper is motivated by the Bayesian non-parametric modelling of the dependence between clustering effects in multiple time series analysis. We follow a Dirichlet process mixture approach and define a new class of multivariate dependent Pitman–Yor processes (DPY). The proposed DPY are represented in terms of vectors of stick-breaking processes which determine dependent clustering structures in the time series. We follow a hierarchical specification of the DPY base measure to account for various degrees of information pooling across the series. We discuss some theoretical properties of the DPY and use them to define Bayesian non-parametric repeated measurement and vector autoregressive models. We provide efficient Monte Carlo Markov Chain algorithms for posterior computation of the proposed models and illustrate the effectiveness of the method with a simulation study and an application to the United States and the European Union business cycle.