APPLIED STATISTICS AND ECONOMETRICS
Instructional goals
The course introduces students to statistical and econometric techniques aimed at empirically addressing economic and financial case studies. Practices, coupled with the use of statistical software for the analysis of real-world data will allow students to gain their abilities in collecting, analyzing, and interpreting macro and micro data.
The course also develops digital competences as of EU DIGCOMP 2.1 (Competence area 1: Information and data literacy; Competence area 2: Communication and collaboration; Competence area 3: Digital content creation).
Intended learning outcomes
Knowledge and understanding: knowledge of data types and related univariate analysis techniques, including simple linear regression model, multiple linear regression model, time-series models.
Applying knowledge and understanding: ability in selecting appropriate data analysis methods, and in analyzing relationships among variables in economics, finance and management
Making judgments: ability in collecting, using and critically interpreting quantitative and qualitative data related to economics, finance and management, achieved through the analysis of documents issued by official national and international statistics, scientific articles on statistical methods and applications, case studies. Digital competences are developed.
Communication skills: ability to spot and present the most suitable framework for the empirical analysis based on the nature of the economic-financial data at hand and effective communication of data analysis results.
Learning skills: ability to learn autonomously data analysis techniques, in professional activities or subsequent studies, achieved through the analysis of econometric methods applied in economics, finance and management.
Course Contents
Theoretical and applied lectures (with real case studies).
Review on Linear Regression with One
Regressor.
Linear Regression with Multiple Regressors.
Hypothesis Tests and Confidence Intervals in Multiple Regression.
The Econometric Theory of Regression Analysis.
Regression Analysis of Economic Time Series Data.
Estimation of Dynamic Causal Effects.
Practices.
Practices are designed to develop the students’ ability in collecting and analyzing data in economics, finance or management, and in analyzing them also by means of statistical software and advanced spreadsheet (Rstudio).
Reference Books
James H. Stock and Mark W. Watson (fourth edition), Introduction to Econometrics, Pearson Ed.
Chapters 4, 5, 6, 7, 15 and 16
Teaching Methods
Lectures, exercises, applied exercises, interactive visualization, case studies in social sciences based on real data, also using statistical and econometric packages
Case studies, Analysis of the literature, Scientific articles, Data from official statistical sources.
Assessment Method
Students will be evaluated based on a final written exam, consisting of both theoretical and empirical questions covering the entire program of the course and a Project Work.
Concerning the type of questions, the written exam will include both open and multiple-choice questions. It is not allowed to consult books or class notes.
There will be no mid-term exam.
During the exam, each candidate will be asked to show a document with a picture (preferably, the university badge). Phones, tablets, etc. should be switched off. It is then required to be endowed with a pocket calculator.
The Project Work will cover a real case study by means of the statistical-econometric software R, and will be done by groups of minimum 3 to maximum 5 students. The Project Work is compulsory and will be evaluated up to 3 points.
The final grade will be computed as the sum of the result of the written examination plus the evaluation obtained in the Project Work (0,1,2,3 points) until the end of the summer session (28th June 2025). Otherwise, the final grade will be given by the evaluation of the written examination only, provided that the Project Work has been done.
After the exam, the final marks will be uploaded in the suitable website and the students will receive a message. After 3 days, marks of students that did not rejected the evaluation will automatically be saved.
Thesis assignment criteria
The degree thesis involves the application of statistical-econometric methodologies in business or economics. The topic and the criteria for its assignment will be discussed with the teacher
Week 1
On campus session
4. Linear Regression with One Regressor
4.1 The Linear Regression Model
4.2 Estimating the Coefficients of the Linear Regression Model 4.3 Measures of Fit
4.4 The Least Squares Assumptions
4.5 Sampling Distribution of the OLS Estimators
Appendix 4.3: Sampling Distribution of the OLS Estimator
Exercises from 4.1 to 4.4
Practice on campus.
Week 2
On campus Session
5. Regression with a Single Regressor: Hypothesis Tests and Confidence Intervals
5.1 Testing Hypotheses About One of the Regression Coefficients
5.2 Confidence Intervals for a Regression Coefficient
5.3 Regression When X Is a Binary Variable
5.4 Heteroscedasticity and Homoscedasticity
5.5 Theoretical Foundations of Ordinary Least Squares
Practice on campus.
Exercises from 5.1 to 5.4
Week 3
On campus session
6. Linear Regression with Multiple Regressors
6.1 Omitted Variable Bias
6.2 The Multiple Regression Model
6.3 The OLS Estimator in Multiple Regression
6.4 Measures of Fit in Multiple Regression
6.5 The Least Squares Assumptions in Multiple Regression
Exercises from 6.1 to 6.5
Practice on campus.
Week 4
On campus session
6.6 Sampling distribution of the OLS estimators in Multiple Regression
6.7 Collinearity
7. Hypothesis Tests and Confidence Intervals in Multiple Regression
7.1 Hypothesis Tests and Confidence Intervals for a Single Coefficient
Exercises from 7.1 to 7.7
Practice on campus.
Week 5
On campus session
7.2 Tests of Joint Hypotheses
7.3 Testing Single Restrictions Involving Multiple Coefficients
Exercises 7.8 and 7.9
Practice on campus.
Week 6
On campus session
Case-study through simple and multiple linear regression models.
7.6 Data analysis on score test
Practice on campus.
Week 7
On campus session
15. Introduction to Time Series Regression and Forecasting
15.1 Introduction to Time Series Data and Serial Correlation
Practice on campus
Week 8
On campus session
15.2 Stationarity and means squared forecast error
15.3 Autoregressions
Practice on campus.
Week 9
On campus session
15.4 Time Series Regression with Additional Predictors and the Autoregressive Distributed Lag Model 15.5 Estimation of the MSFE and Forecast Intervals
15.6 Lag Length Selection Using Information Criteria
Practice on campus..
Week 10
On campus session
15.7 Nonstationarity I: Trends
Practice on campus.
Week 11
On campus session
15.8 Nonstationarity II: structural
Case-study through univariate linear time-series processes.
Practice on campus.
Week 12
On campus session
16. Estimation of Dynamic Causal Effects
16.1 An Initial Taste of the Orange Juice Data
16.2 Dynamic Causal Effects
16.3 Estimation of Dynamic Causal Effects with Exogenous Regressors
Discussion of Scientific Articles, possible review on the explained topics, Case-study with cross-sectional and time-series data.
Practice on campus.