Computational Laboratory for Economics
Prof. Gabriele Cantaluppi
COURSE AIMS
Make participants able to implement statistical procedures in order to properly solve economic and business problems by means of standard software. The course runs in parallel with the Empirical Economics where students will learn the basics of the theory and practice of econometrics.
COURSE CONTENTS
Module I
1.Basic elements of computer programming: the software R.
–Data input and Data frames; Summary statistics; Pseudo-random number generation routines.
2.Linear models: implementation, extensions and generalizations:
–Presence of dummy variables, endogeneity and heteroscedasticity.
–Generalized linear models (Logit, Probit).
–Proportional odds logistic regression (Ordered Logit and Probit).
–Multinomial Logit model.
–Tobit regression models for censored data.
–Sample selection models.
Module II
1.Univariate Time Series Models:
–Plotting data, transforming data, autocorrelation and partial autocorrelationfunctions analysis.
–Simulation of stochastic processes: Gaussian White Noise, White Noise, Auto Regressive (AR) and Moving Average (MA).
–Analysis of the autocorrelation functions; Test for white noise.
–Identification of the ARMA element modelling a stationary time series.
–Test for Stationarity against the presence of Unit Roots.
–Estimation of ARMA Models.
–Model Selection Criteria.
–Forecasting with ARMA models
–Autoregressive Conditional Heteroskedasticity.
2.Multivariate Time Series Models:
–Dynamic Models with Stationary Variables.
–Models with Nonstationary Variables.
–Vector Autoregressive Models.
–Cointegration.
3.Models Based on Panel Data:
–Static and Dynamic Linear and Non-Linear Models.
–Nonstationarity, Unit Roots and Cointegration.
4.Special topics.
–Testing for cointegration with structural breaks.
–Bootstrap Methods in Econometrics.
READING LIST
M. Verbeek, A Guide to Modern Econometrics,John Wiley, NY, 2008, 3rd ed.
G. Cantaluppi, An Introduction to R, EDUCatt, 2010.
G. Cantaluppi, Computational Laboratory for Economics. Lecture Notes, EDUCatt, 2011.
A. Zeileis, Applied Econometrics with R, Springer-Verlag, New York, 2008.
J.J. Faraway, Practical Regression and Anova using R,
B. Pfaff, VAR, SVAR and SVEC Models: Implementation Within R Package vars, Journal of Statistical Software, vol. 27, 4, 2008,
Y. Croissant-G. Millo, Panel Data Econometrics in R: The plm Package, Journal of Statistical Software, vol. 27, 2, 2008,
R.A. Johnson-D.W. Wichern, Applied Multivariate Statistical Analysis, Prentice Hall, NJ, 2002.
TEACHING METHOD
Lectures and assignments in the lab.
ASSESSMENT METHOD
Project work/final research paper, laboratory and oral exam.
NOTES
Further information can be found on the lecturer's webpage at or on the Faculty notice board.
Computational Methods for the Analysis of Business and Economic Data
Prof. Gabriele Cantaluppi; Prof. Chiara Paolino
Module I:Prof. Gabriele Cantaluppi
COURSE AIMS
Make participants able to implement statistical procedures in order to properly solve economic and business problems by means of standard software.
COURSE CONTENTS
1.Basic elements of computer programming: the software R.
–Data input and Data frames; Summary statistics; Pseudo-random number generation routines.
2.Linear models: implementation, extensions and generalizations:
–Presence of dummy variables, endogeneity and heteroscedasticity.
–Generalized linear models (Logit, Probit).
3.Exploratory data analysis tools: principal components, factor analysis and cluster analysis.
READING LIST
M. Verbeek, A Guide to Modern Econometrics, John Wiley, NY, 2008, 3rd ed.
G. Cantaluppi, An Introduction to R, EDUCatt, 2010.
G. Cantaluppi, Computational Laboratory for Economics. Lecture Notes, EDUCatt, 2011.
J.J. Faraway, Practical Regression and Anova using R,
R.A. Johnson-D.W. Wichern, Applied Multivariate Statistical Analysis, Prentice Hall, NJ, 2002.
A.M. Mood-F.A. Graybill-D.C. Boes, Introduction to the Theory of Statistics, McGraw-Hill, 1974.
TEACHING METHOD
Lectures and assignments in the lab. A preliminary tutorial on inferential statistics is scheduled at the beginning of the course.
ASSESSMENT METHOD
Project work/final research paper, laboratory and oral exam.
NOTES
Further information can be found on the lecturer's webpage at or on the Faculty notice board.