Alessandra Luati

Alessandra Luati, PhD

Adjunct Professor of Statistics

SAIS Europe

Expertise

Topics
  • Statistics
Languages
  • Italian

Background and Education

Professor of Statistics, University of Bologna; Director of the PhD program in Statistics, University of Bologna. Professor Luati is Associate Editor of Statistical Methods and Applications, since 2014. She was Visiting professor at: University of Aarhus (DK), University of Cambridge (UK), Imperial College Business school (UK), Waseda University (Japan), Joint Research Centre of the European Commission (Ispra, Italy), University of Glasgow (UK), University of St Andrews (UK). Professor Luati was Visiting Erskine fellow at the University of Canterbury (NZ) 2014; Principal Investigator for the unit of Bologna for the project "Forecasting economic and financial time series: understanding the complexity and modelling structural change," PRIN 2011; Co-chair of the Ercim working group in Statistical signal extraction and filtering, from 2012.

Publications
Professors Luati's publications include: "Robust time series models with trend and seasonal components," in SERIEs, with M. Caivano and A. Harvey (2016); "The Generalised Autocovariance Function," in Journal of Econometrics, with T. Proietti (2015); "Spectral filtering for trend estimation," in Linear Algebra and its Applications, with M. Donatelli and A. Martinelli (2015); "Filtering with Heavy Tails," in Journal of the American Statistical Association, with A. Harvey (2014); "The Variance Profile," in Journal of the American Statistical Association, with T. Proietti and M. Reale (2012); "An Approximate Quantum Cramér-Rao Bound Based on Skew Information," in Bernoulli (2011); "Hyper-spherical and Elliptical Stochastic Cycles," in Journal of Time Series Analysis, with T. Proietti (2010); "On the spectral properties of matrices associated with trend filters," in Econometric Theory, with T. Proietti (2010); "A cascade linear filter to reduce revisions and turning points for real time trend-cycle estimation," in Econometric Reviews, with E.B. Dagum (2009); "Real Time Estimation in Local Polynomial Regression with an Application to Trend-Cycle Analysis," in Annals of Applied Statistics, with T. Proietti (2008); "Maximum Fisher information in mixed state quantum systems," in Annals of Statistics (2004). For more publications, see personal webpage.


2017-08-15 00:00:00 
...
Spring 2018 
Aim of the cour...
Aim of the course is to introduce the basic statistical tools required to conduct and evaluate empirical research in economics and the social sciences. The topics that will be covered include elementary probability theory, sampling, estimation, hypothesis testing, correlation and regression. Special attention will be given to the application of these statistical tools to the analysis of real phenomena. In particular, in order to obtain a better understanding of the concept taught in lectures, an emphasis is placed on using software such as Excel and STATA. This course is a prerequisite for more advanced courses in econometrics. (Cross listed International Economics/International Development.)