• Edizioni di altri A.A.:
  • 2015/2016
  • 2016/2017
  • 2017/2018
  • 2018/2019
  • 2019/2020
  • 2020/2021
  • 2021/2022
  • 2021/2022
  • 2023/2024
  • 2024/2025
  • 2025/2026
  • 2026/2027

  • Language:

    Italian
     
  • Textbooks:

    Notes from class.
    Wooldridge, J. (2013) Introductory Econometrics, a Modern Approach, Cengage
    Kleiber C. Zeileis A. (2008) Applied Econometrics with R, Springer, UseR! series
    Kelejian H. Piras G. (2017) Spatial Econometrics, Elsevier
    Additional material may be suggested in class.
     
  • Learning objectives:

    The course aims at the intellectual development process of the student by providing essential tools to understand and analyze economic data. In turn, understanding and analyzing data will allow student to make rational decisions over a range of economic problems at various levels. The course aims at providing basic methodological and practical knowledge in econometrics. Real world examples will be proposed and analyzed using the statistical software R.
    At the end of the semester students will be able to apply what they have learned throughout the course to solve complex economic problems.
     
  • Prerequisite:

    Basic statistic and probability
     
  • Teaching methods:

    Lectures, exercise in R.
     
  • Exam type:

    Written and oral.
    The written part on the entire program consists of theoretical questions and additional R problems.
     
  • Sostenibilità:
     
  • Further information:

    e-mail: gianfranco.piras@unich.it Office hours is by appointment only.
     


1. The nature of Econometrics and typology of economic data.
2. Simple linear regression model and introduction on estimation methods: OLS, GMM and ML
3. Multiple linear regression model: statistical inference and testing of hypothesis
4. Multiple regression and functional forms
5. Linear regression with dummy variables
6. Cross-sectional correlation and an introduction to spatial econometrics models
7. Introduction on panel data
8. Introduction to the R software environment.


1. What is econometrics and steps in the empirical analysis.
2. Structure of economic data: cross-section, time series and panel data 3. Definition and derivation of the simple linear regression model:
- Properties of OLS
- Units of measurements and functional form
- Expected value and variance of the OLS
4. Definition and derivation of the simple linear regression model:
- Mechanics and interpretation of the coefficients
- Comparison between single and multiple regression model
- Expected value and variance of the OLS.
- Efficiency of the OLS and the Gauss-Markov theorem
5. Inference of the linear regression model
- Hypothesis test on a single coefficient
- Testing hypothesis about a single linear combination of the parameters
- Testing multiple linear restrictions
6. More on the multiple regression model
- Effects of data scaling
- Functional form: Logs, quadratic form and interactions between variables
7. Linear regression using dummy variables: models with a single dummy, models with multiple dummies, binary dependent variable models
8. Introduction to spatial models
- Definition of the spatial weights matrix
- Specification of spatial models: SARAR, LAG, ERROR, MIXED etc.
- Estimation methods for spatial models: Instrumental variables and Maximum likelihood
9. Panel data models: pooled, fixed effects and random effects

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