Baysian Model Averaging, Learning and Model Selection

Agents have two forecasting models, one consistent with the unique rational expectations equilibrium, another that assumes a time-varying parameter structure. When agents use Bayesian updating to choose between models in a self-referential system, we find that learning dynamics lead to selection of one of the two models. However, there are parameter regions for which the non-rational forecasting model is selected in the long-run. A key structural parameter governing outcomes measures the degree of expectations feedback in Muth's model of price determination.

Horizontal Agreements and R&D Complementarities: Merger versus RJV

We study the decision of two rms within an oligopoly concerning whether to enter into a horizontal agreement to exploit complementarities between their R&D activities and, if so, whether to merge or form a research joint venture (RJV). In contrast to horizontal merger, there is a probability that an RJV contract will fail to enforce R&D sharing. We nd that a horizontal agreement always arises. The insiders' merger/RJV choice involves a trade-o : While merger o ers certainty that R&D complementarities will be exploited, it leads to a pro t-reducing reaction by outsiders on the product market, where competition is Cournot. Greater brand similarity and contract enforceability (\quality") both favour RJV, while greater R&D complementarity favours merger. Interestingly, the insiders may choose to merge even when RJV contracts are always enforceable, and they may opt to form an RJV even when the likelihood of enforceability is negligible.

Legal Uncertainty, Competition Law Enforcement Procedures and Optimal Penalties

In this paper we make three contributions to the literature on optimal Competition Law enforcement procedures. The first (which is of general interest beyond competition policy) is to clarify the concept of “legal uncertainty”, relating it to ideas in the literature on Law and Economics, but formalising the concept through various information structures which specify the probability that each firm attaches – at the time it takes an action – to the possibility of its being deemed anti-competitive were it to be investigated by a Competition Authority. We show that the existence of Type I and Type II decision errors by competition authorities is neither necessary nor sufficient for the existence of legal uncertainty, and that information structures with legal uncertainty can generate higher welfare than information structures with legal certainty – a result echoing a similar finding obtained in a completely different context and under different assumptions in earlier Law and Economics literature (Kaplow and Shavell, 1992). Our second contribution is to revisit and significantly generalise the analysis in our previous paper, Katsoulacos and Ulph (2009), involving a welfare comparison of Per Se and Effects- Based legal standards. In that analysis we considered just a single information structure under an Effects-Based standard and also penalties were exogenously fixed. Here we allow for (a) different information structures under an Effects-Based standard and (b) endogenous penalties. We obtain two main results: (i) considering all information structures a Per Se standard is never better than an Effects-Based standard; (ii) optimal penalties may be higher when there is legal uncertainty than when there is no legal uncertainty.