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CEIS: CENTRE FOR ECONOMIC & INTERNATIONAL STUDIES
Furio Camillo Rosati - Director "How Does the Public Spending Affect Technical Efficiency? Some Evidence from 15 European Countries" 
CEIS Working Paper No. 476 SABRINA AUCI, University of Palermo - Department of European Studies and International Integration.
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LAURA CASTELLUCCI, University of Rome Tor Vergata
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MANUELA COROMALDI, University of Rome II
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The relationship between government size and economic growth has been widely debated. Departing from this issue, we provide an empirical analysis of the impact of government size on technical efficiency. The aim of this paper is to estimate by using a True Random Effect model the impact of public sector’s size and of public expenditure components on 15 European countries’ technical efficiency from 1996 to 2011. Using the total public expenditure as a proxy for the government size we estimate simultaneously national optimal production function and technical efficiency model by controlling for income distribution and institutional quality. Our main findings show that the effect of public sector’s size on efficiency is positive while the type of public expenditures may have both positive and negative impact. In more details, results suggest that social protection, cultural, and health expenditures have a positive effect on technical efficiency, while others have a negative impact. More controversial is the impact of education expenditure, even if a positive effect on efficiency prevails when controlling for heteroscedasticity. "Likelihood Induced by Moment Functions using Particle Filter: A Comparison of Particle GMM and Standard MCMC Methods"
CEIS Working Paper No. 477 FABIO FRANCO, Independent
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Particle
filtering is a useful statistical tool which can be used to make inference on the latent variables and the structural parameters of state space models by employing it inside MCMC algorithms (Flury and Shephard, 2011). It only relies on two assumptions (Gordon et al, 1993): a: The ability to simulate from the dynamic of the model; b: The predictive measurement density can be computed. In practice the second assumption may not be obvious and implementations of particle filter can become difficult to conduct. Gallant, Giacomini and Ragusa (2016) have recently developed a particle filter which does not rely on the structural form of the measurement equation. This method uses a set of moment conditions to induce the likelihood function of a structural model under a GMM criteria. The semiparametric structure allows to use particle filtering where the standard techniques are not applicable or difficult to implement. On the other hand, the GMM representation is less efficient than the standard technique and in some cases it can affect the proper functioning of particle
lter and in turn deliver poor estimates. The contribution of this paper is to provide a comparison between the standard techniques, as Kalman filter and standard bootstrap particle filter, and the method proposed by Gallant et al (2016) in order to measure the performance of particle filter with GMM representation. | | ^top
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Distributed by Economics Research Network (ERN), a division of Social Science Electronic Publishing (SSEP) and Social Science Research Network (SSRN) Directors ECONOMICS RESEARCH CENTERS PAPERS MICHAEL C. JENSEN
Harvard Business School, SSRN, National Bureau of Economic Research (NBER), European Corporate Governance Institute (ECGI), Harvard University - Accounting & Control Unit
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