66056 - Laboratory of Economic Statistics and Market Analysis

Learning outcomes

This course will present multivariate statistical methods in several ways: (i) It offers an overview of the main multivariate methods in economics and business, especially for microdata, (ii) It helps in choosing the best statistical method for different economic data sets, (iii) A brief explanation of some advanced methods, like spatial modeling, are also included, (iv) The use of the software R will help in estimating models, testing, and displaying related graphs.

Course contents

Introduction to data: definition, types and sources, microdata, main issues related to observational data.

Linear models: intro, Gauss-Markov hypotheses and inference, Monte Carlo simulations (an example), marginal effects, dummy variables, model selection, LASSO, economic data examples. Residual analysis and specification tests, violation of the hypoteses and alternative estimators, endogeneity test, exogeneity test, endogeneity example.

Nonlinear models: nonlinear models in regressors, marginal effects, limited dependent variable models (binary probit/logit , multinomial, ordered, count data, censured/truncated data), power transformations. Seemingly unrelated regressions (SUR) models, spatial binary probit models, Diff-in-Diff.

Time series: intro, residual analysis and specification tests, time series components, forecasting with classical methods, missing data imputation with moving average, univariate models with an example of the electricity market.

Panel data models: intro, one way error component: individual/temporal fixed effects and random effects, two way error component: individual and temporal fixed effects and random effects.

R programming

 

 

 

Readings/Bibliography

References:

A. Colin Cameron, Pravin K. Trivedi (2005),
Microeconometrics: Methods and Applications, Cambridge
University Press.

Marno Verbeek (2005), Econometria, I edizione, Zanichelli Editore.

William Greene (2019), Econometric Analysis, Pearson. Eighth
Edition (Global Edition).

Gareth James, Daniela Witten, Trevor Hastie, Robert
Tibshirani (2021), An Introduction to Statistical Learning with
Applications in R, Springer.

 

Teaching methods

Lessons are carried out considering both methodological and empirical aspects in economics, with the help of the statistical software R.

The used economic datasets are all available in R or provided by the Professor.

Assessment methods

Oral examination.

The examination consists into the evaluation of a work group.

The students are divided into groups and they will prepare a short presentation in power point explaining the type of dataset used, the statistical method/s and the results with the use of R.

During the presentation some questions about the arguments of the course are made.

Teaching tools

Pc; videoprojector.

Office hours

See the website of Anna Gloria Billè