APPLIED STATISTICS AND ECONOMETRICS
Instructional goals
The course aims to provide a foundation in statistical and econometric analysis as to highlight its potential applications in economics and finance. Throughout the course, students will be encouraged, by exercises and the use of appropriate software, to apply theoretical and methodological concepts to solve empirical problems in the analysis of macro- and micro-data.
The course develops digital skills related to the European DIGCOMP 2.1 standard (Skill Area 1: Information and Data Literacy; Skill Area 2: Communication and Collaboration; Skill Area 3: Digital Content Creation).
Intended learning outcomes
Data Analysis and Descriptive Statistics, Probability, Inference, Simple Linear Regression.
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
The assessment will be based on a written exam (Scritto Verbalizzante), which will cover all the topics addressed during the course. The knowledge acquired will be evaluated through answers to theoretical questions, questions involving the solution of quantitative exercises, and questions requiring the interpretation of estimates provided through the statistical-econometric software output. Regarding the types of questions, the written exam may include both open-ended questions and multiple-choice questions.
Up to two additional points may be added to the score obtained in the written exam through a Project Work (PW). The PW requires the application of the methodologies presented during the course to empirical data, with the support of the open-source statistical software R. The PW will focus on cross-sectional data and will be carried out in teams consisting of a minimum of 3 to a maximum of 5 students. The points obtained in the PW remain valid until the end of the summer exam session. The PW must be submitted via the myluiss platform by the end of the mid-term teaching break week.
During the written exam, consulting books or notes, as well as the use of mobile phones and tablets, will not be permitted; however, students are advised to bring a calculator.
At the end of the examination process, the instructor publishes the results on the platform dedicated to online grade registration. The system sends a notification with the results to the candidates (the results of the written exam may also be viewed via web self-service).
From the moment the results are published, candidates have three days to reject the grade. After this period, the rule of tacit consent applies, and the grade is officially recorded by the instructor, who must finalize the grade report through a digital signature (the system sends a reminder to the instructor to complete this operation). Once the report is closed, the candidate receives a confirmation email of the grade obtained.
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.