Università Roma Tre

Part I: Descriptive Statistics, Probability and Inference
1. Probability: definition and events. Conditional Probability
2. Random variables: Discrete and Continuous random variables (probability
mass function, probability density function, expectation and
variance). Linear transformations of random variables
3. Sampling distributions. Estimators and their properties. Confidence
intervals and hypothesis testing
References in the course textbook: Appendix A and B (skip “kurtosis”
and paragraph 8.4) and C
Part II: Statistical models
1. Simple Linear Regression: Estimation and Inference. Assessment of
the goodness of Fit
2. Multiple Linear regression model: Estimation (main principle) and inference.
Gauss Markov Theorem. Assessment of the goodness of Fit
3. Model interpretation: the role of the regression coefficients
4. Variable transformations: log(y), transforming the predictors (log(x),
x2). Variable standardisation: centering and standardising the predictors.
Categorical variables in multiple regression
5. Model selection: The F-test for nested models
References in the course textbook: Chapters 1,2,3,4,5,6,7 (Skip
the “Linear Probability Model”
Part III: Advanced topics
1. Heteroskedasticity: meaning and consequences on OLS estimation. Robust
standard errors. Testing for heteroskedasticity (Breusch Pagan
Test)
2. Weighted Least Squares: estimating the heteroscedasticity function
3. Simple panel data methods (skip “Chow test for structural changes”)
References in the course textbook: Chapter 8 (Skip, as before,
“Linear Probability Model”), Chapter 13, (skip “Chow test for structural
changes”), Chapter 14 (Only the first paragraph, the second is optional)
Part IV: R programming
1. Basic R functions: basic algebraic operations, mean variance, median
2. R structures: dataframe, factor variables
3. Basic plots: scatterplot, histograms and boxplots
4. Linear Models in R: lm function
References: comprare the scripts uploaded in the Teams Chanel
of the course
References:
ˆ Introductory Econometrics- A modern Approach J.M. Wooldridge -
South-Western cengage learning
ˆ Slides and script available in the Teams Channel of the Course