1,721,028 research outputs found
Moment conditions and neglected endogeneity in panel data models. WP Series University of Verona Department of Economics (ISSN 2036-4679)
No full textIdentification of linear panel data models when instruments are not available. WP Series University of Verona Department of Economics (ISSN 2036-4679)
No full textAutocorrelation and masked heterogeneity in panel data models estimated by maximum likelihood. WP Series University of Verona Department of Economics (ISSN 2036-4679)
No full textInternational cooperation in pharmaceutical research. WP Series University of Verona Department of Economics (ISSN 2036-4679)
No full textAutocorrelation and masked heterogeneity in panel data models estimated by maximum likelihood
No full textIn a panel data model with random effects, when autocorrelation in the error is considered, (Gaussian) maximum likelihood estimation produces a dramatically large number of corner solutions: the variance of the random effect appears (incorrectly) to be zero, and a larger autocorrelation is (incorrectly) assigned to the idiosyncratic component. Thus heterogeneity could (incorrectly) be lost in applications to panel data with customarily available time dimension, even in a correctly specified model. The problem occurs in linear as well as nonlinear models. This article aims at pointing out how serious this problem can be (largely neglected by the panel data literature). A set of Monte Carlo experiments is conducted to highlight its relevance, and we explain this unpleasant effect showing that, along a direction, the expected log-likelihood is nearly flat
Testing Initial Conditions in Dynamic Panel Data Models
No full textWe propose a new framework for testing the ``mean stationarity'' assumption in dynamic panel data models, required for the consistency of the system GMM estimator. In our set up the assumption is obtained as a parametric restriction in an extended set of moment conditions, allowing the use of a LM test to check its validity. Our framework provides a ranking in terms of power of the analyzed test statistics, in which our approach exhibits better power than the difference-in-Sargan/Hansen test that compares system GMM and difference GMM, that is, on its turn, more powerful than the Sargan/Hansen test based on the system GMM moment conditions
Learning from Failures or Failing to Learn? Lessons from Pharmaceutical R&D
No full textInnovation is a trial and error process in which both successes and failures contribute to knowledge creation and destruction. In this paper we test theoretical predictions about the role of failures in new product development on private and public knowledge and interfirm knowledge transfer. We analyse the outcomes of world-wide R&D projects in the pharmaceutical industry, and proxy knowledge flows with forward citations received by patents associated with each project. We find that patents covering successfully completed projects (i.e., leading to drug launch on the market) receive more citations than those associated to failed (terminated) projects, which in turn are cited more often than patents lacking clinical or preclinical information. Failures by specialized firms are cited more frequently than the ones of generalist companies. We therefore offer evidence of the value of failures as research inputs in (pharmaceutical) innovation
The Sustainability of European Health Care Systems: Beyond Income and Ageing. WP Series University of Verona Department of Economics (ISSN 2036-4679)
No full textThe productivity crisis in pharmaceutical R&D. WP Series University of Verona Department of Economics (ISSN 2036-4679)
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