nep-ict 2007-09-09 papers

on Information and Communication Technologies

Issue of 2007‒09‒09
two papers chosen by
Walter Frisch
University Vienna

Competition Policy Implications of Electronic Business-to-Business Marketplaces: Issues for Marketers By Andrew Pressey; John K. Ashton
Bayesian Inference in a Cointegrating Panel Data Model By Gary Koop; Roberto Leon-Gonzalez; Rodney Strachan

Competition Policy Implications of Electronic Business-to-Business Marketplaces: Issues for Marketers

By:	Andrew Pressey (Norwich Business School and Centre for Competition Policy, University of East Anglia); John K. Ashton (Centre for Competition Policy, University of East Anglia)
Abstract:	Electronic marketplaces (e-marketplaces) allow networks of buyers and sellers to conduct business online and to exchange information more efficiently using Internet technology. Despite the benefits that e-marketplaces potentially afford firms, concerns have been raised that these markets may damage competition. This study considers the antitrust or competition legislation related to e-marketplaces and examines the possible competition concerns they raise. Potentially anticompetitive features of e-marketplaces are examined and guidance for firm conduct when creating or participating in an e-marketplace is offered.
Keywords:	Electronic marketplaces, antitrust, policy
JEL:	M31 K21 L42
Date:	2007–06
URL:	http://d.repec.org/n?u=RePEc:ccp:wpaper:wp07-15&r=ict

Bayesian Inference in a Cointegrating Panel Data Model

By:	Gary Koop (University of Strathclyde, UK and Rimini Centre for Economic Analysis, Rimini, Italy); Roberto Leon-Gonzalez (University of Leicester, UK and University of Queensland); Rodney Strachan (University of Queensland)
Abstract:	This paper develops methods of Bayesian inference in a cointegrating panel data model. This model involves each cross-sectional unit having a vector error correction representation. It is flexible in the sense that different cross-sectional units can have different cointegration ranks and cointegration spaces. Furthermore, the parameters which characterize short-run dynamics and deterministic components are allowed to vary over cross-sectional units. In addition to a noninformative prior, we introduce an informative prior which allows for information about the likely location of the cointegration space and about the degree of similarity in coefficients in different cross-sectional units. A collapsed Gibbs sampling algorithm is developed which allows for efficient posterior inference. Our methods are illustrated using real and artificial data.
Keywords:	Bayesian, panel data cointegration, error correction model, reduced rank regression, Markov Chain Monte Carlo.
JEL:	C11 C32 C33
Date:	2007–07
URL:	http://d.repec.org/n?u=RePEc:rim:rimwps:02-07&r=ict

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