Federico Belotti

I am a post-doc researcher at the Centre for Economics and International Studies of the University of Rome Tor Vergata, where I received a B.A. in Economics (2004), a M.Sc. in Quantitative Methods (2005) and a Ph.D. in Econometrics (2011). Prior to joining my Ph.D. program, I was a researcher at the Italian National Institute of Statistics (ISTAT), where I have been involved in the design and management of major italian surveys.

My research interests lie in the field of econometrics with applications in health, spatial and production economics. My doctoral dissertation collects two independent contributions. The first, finalized during my visiting at the Department of Economics of the City University of New York, focuses on finite mixtures of regression models and uses the minimum density power divergence framework to draw robust inference. The second concentrates on fixed-effects stochastic frontier models proposing alternative estimation strategies to solve the incidental parameters problem. For these studies, I was awarded from the Italian Statistical Society with the prize for the "Best Italian Ph.D. Thesis on Applied Statistics" in 2012, and with the "Best Young Researcher Paper Award" at the XII European Workshop on Efficiency and Productivity Analysis.

My ongoing research is mostly based on ideas arising from the theory of composite likelihood estimation. For instance, by exploiting the intrinsic robustness of the pairwise conditional maximum likelihood approach, one of these studies proposes a specification test for the null hypothesis of time-invariant unobserved heterogeneity in generalized linear panel data models. Among other things, I am currently working on the composite likelihood estimation of the spatial error stochastic frontier model, the pairwise estimation of spatial panel data models, robust stochastic frontier analysis and fixed-effects quantile regression.

On a different ground, I have been recently involved as econometrician in an international project aiming to develop a dynamic microsimulation model to predict the future costs and health status of the elderly in Europe by adapting the core of the Future Elderly Model. I am also currently serving as a consultant for the Agricultural Development Economics Department at the FAO, where I am conducting empirical analyses on the Climate-Smart Agriculture topic.

I am the primary author of three popular Stata commands providing a significant addition to Stata's capabilities in terms of estimation of cross-sectional and panel data stochastic frontier models, spatial panel data models and cross-sectional two-parts models. Beyond Stata (and Mata), I am also advanced with other matrix and object-oriented languages, like R, Matlab and C++.