if this message does not display correctly, click here | Table of Contents Gianluca Cubadda, University of Rome II - Department of Economics and Finance
Barbara Guardabascio, University of Rome, Tor Vergata Walter Ferrarese, University of Rome, Tor Vergata - Department of Economics, Law and Institutions Vincenzo Mollisi, University of Rome, Tor Vergata
Gabriele Rovigatti, University of Rome, Tor Vergata | |
CEIS: CENTRE FOR ECONOMIC & INTERNATIONAL STUDIES
Vincenzo Atella - Director "Representation, Estimation and Forecasting of the Multivariate Index-Augmented Autoregressive Model" 
CEIS Working Paper No. 397 GIANLUCA CUBADDA, University of Rome II - Department of Economics and Finance
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BARBARA GUARDABASCIO, University of Rome, Tor Vergata
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We examine the conditions under which each individual series that is generated by a vector autoregressive model can be represented as an autoregressive model that is augmented with the lags of few linear combinations of all the variables in the system. We call this modelling Multivariate Index-Augmented Autoregression (MIAAR). We show that the parameters of the MIAAR can be estimated by a switching algorithm that increases the Gaussian likelihood at each iteration. Since maximum likelihood estimation may perform poorly when the number of parameters gets larger, we propose a regularized version of our algorithm to handle a medium-large number of time series. We illustrate the usefulness of the MIAAR modelling both by empirical applications and simulations. "When Multiple Merged Entities Lead in Stackelberg Oligopolies: Merger Paradox and Welfare"
CEIS Working Paper No. 398 WALTER FERRARESE, University of Rome, Tor Vergata - Department of Economics, Law and Institutions
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The merger paradox refers to the fact that in a symmetric static Cournot oligopoly horizontal mergers are generally unprofitable. Moreover, even in case of profitable mergers, remaining outside the merger is better than participating (free-riding issue). In this paper we tackle both issues in a model with linear inverse demand, in which we allow for multiple simultaneous mergers from a static symmetric Cournot market. Once the mergers occur, each merged entity acquires the right of becoming the leader over the remaining firms outside the mergers (outsiders). We allow the leaders to be heterogeneous in the number of members (insiders). Our model connects and extends Liu and Wang (2015), who are the first to explore the feature of the leadership acquisition. They show that if a unique merged entity acquires the leadership, then there is always an incentive for such merger to occur. However, they do not tackle the free riding aspect of mergers. We obtain that the case of a unique leader is the only one in which the merged entity has always an incentive to form. We carry out a welfare analysis and show that, in our setting, despite the symmetry of firms total output can often rise and make consumers better off. Moreover, the adoption of consumers surplus only or consumer surplus plus industry profits as welfare measures does not change the set of welfare improving mergers. This suggests that the common view on horizontal mergers among symmetric firms being unambiguously welfare reducing requires, in some cases a deeper analysis, since the change in the market structure alone can be enough to increase welfare. It also suggests parsimony for the antitrust authorities in evaluating the welfare implications of mergers. "Theory and Practice of TFP Estimation: The Control Function Approach Using Stata"
CEIS Working Paper No. 399 VINCENZO MOLLISI, University of Rome, Tor Vergata
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GABRIELE ROVIGATTI, University of Rome, Tor Vergata
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Alongside Instrumental Variable (IV) and Fixed Effects (FE), the Control Function (CF) approach is the most widely used in production function estimation. Olley-Pakes, Levinsohn-Petrin, Ackerberg-Caves-Frazer have all contributed to the literature proposing two-steps estimation procedures, while Wooldridge showed how to perform a consistent estimation within a single step GMM framework. In this paper we propose a new estimator, based on Wooldridge's, using dynamic panel instruments à la Blundell-Bond and we evaluate its performance by Monte Carlo simulations. We also present a new Stata module - prodest - for production function estimation, show its main features and key strengths in a comparative analysis with other available user-written commands. Lastly, we provide evidence of the numerical challenges faced when using OP/LP estimators with ACF correction in empirical applications and document how the GMM estimates vary depending on the optimization/starting points used. | | ^top
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