The main objective of the course is to provide the fundamental tools for the application of statistical methods to the analysis of economic data. The theoretical part will be supported by an applied part devoted to the analysis of real data sets by means of the software R. One lecture per week will be held in the computer lab. A student that has completed the course should be practiced in the application of advanced statistical methods, should be able to interpret the results of a statistical analysis, and should be aware of limitations and possible sources of errors in the analysis.
Curriculum
teacher profile teaching materials
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:
Introductory Econometrics- A modern Approach J.M. Wooldridge -
South-Western cengage learning
Slides and script available in the Teams Channel of the Course
Slides and R scripts provided by the instructor
Mutuazione: 21210065 Statistical methods in economics in Economia dell'ambiente, lavoro e sviluppo sostenibile LM-56 R Dotto Francesco
Programme
Part I: Descriptive Statistics, Probability and Inference1. 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:
Introductory Econometrics- A modern Approach J.M. Wooldridge -
South-Western cengage learning
Slides and script available in the Teams Channel of the Course
Core Documentation
Wooldridge, Jeffrey M.,. "Introductory econometrics: A modern approach." (2009)Slides and R scripts provided by the instructor
Type of evaluation
Two different assessment methods are available: 1) Written examination + Project Work: The written examination, lasting two hours, consists of 6–7 exercises and/or theoretical questions covering the topics presented during the course. The maximum score that can be obtained in the written examination is 24/30. The project work consists of preparing a written report to be submitted to the instructor, followed by an oral presentation. Both the report and the oral presentation must be carried out in groups of a minimum of three and a maximum of six students. The maximum score that can be obtained for the project work is 8/30. 2) Written examination (see point 1 above for details), supplemented by additional questions concerning the interpretation of statistical output and the writing of commands in the statistical software R. teacher profile teaching materials
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:
Introductory Econometrics- A modern Approach J.M. Wooldridge -
South-Western cengage learning
Slides and script available in the Teams Channel of the Course
Slides and R scripts provided by the instructor
Mutuazione: 21210065 Statistical methods in economics in Economia dell'ambiente, lavoro e sviluppo sostenibile LM-56 R Dotto Francesco
Programme
Part I: Descriptive Statistics, Probability and Inference1. 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:
Introductory Econometrics- A modern Approach J.M. Wooldridge -
South-Western cengage learning
Slides and script available in the Teams Channel of the Course
Core Documentation
Wooldridge, Jeffrey M.,. "Introductory econometrics: A modern approach." (2009)Slides and R scripts provided by the instructor
Type of evaluation
Two different assessment methods are available: 1) Written examination + Project Work: The written examination, lasting two hours, consists of 6–7 exercises and/or theoretical questions covering the topics presented during the course. The maximum score that can be obtained in the written examination is 24/30. The project work consists of preparing a written report to be submitted to the instructor, followed by an oral presentation. Both the report and the oral presentation must be carried out in groups of a minimum of three and a maximum of six students. The maximum score that can be obtained for the project work is 8/30. 2) Written examination (see point 1 above for details), supplemented by additional questions concerning the interpretation of statistical output and the writing of commands in the statistical software R. teacher profile teaching materials
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:
Introductory Econometrics- A modern Approach J.M. Wooldridge -
South-Western cengage learning
Slides and script available in the Teams Channel of the Course
Slides and R scripts provided by the instructor
Mutuazione: 21210065 Statistical methods in economics in Economia dell'ambiente, lavoro e sviluppo sostenibile LM-56 R Dotto Francesco
Programme
Part I: Descriptive Statistics, Probability and Inference1. 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:
Introductory Econometrics- A modern Approach J.M. Wooldridge -
South-Western cengage learning
Slides and script available in the Teams Channel of the Course
Core Documentation
Wooldridge, Jeffrey M.,. "Introductory econometrics: A modern approach." (2009)Slides and R scripts provided by the instructor
Type of evaluation
Two different assessment methods are available: 1) Written examination + Project Work: The written examination, lasting two hours, consists of 6–7 exercises and/or theoretical questions covering the topics presented during the course. The maximum score that can be obtained in the written examination is 24/30. The project work consists of preparing a written report to be submitted to the instructor, followed by an oral presentation. Both the report and the oral presentation must be carried out in groups of a minimum of three and a maximum of six students. The maximum score that can be obtained for the project work is 8/30. 2) Written examination (see point 1 above for details), supplemented by additional questions concerning the interpretation of statistical output and the writing of commands in the statistical software R.