INTRODUCTION TO TIME SERIES ECONOMETRICS
Syllabus
EN
IT
Learning Objectives
LEARNING OUTCOMES:
The course lays the foundamentals for the statistical and econometric analysis of both sectional and time series data, highlighting the potential applications to economic and
financial research questions.
Contents cover the Linear Regression Model, in both simple and multiple specification, with its assumptions and properties. As for the time-series analysis, the course introduces
the notions of auto-correlation, heteroskedasticity and non-stationarity, analysing the consequences in terms of model estimation outcomes and illustrating how to properly deal
with those features in the data analysis.
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 area1: Information and data literacy; Competence area 2: Communication and collaboration;
Competence area 3: Digital content creation).
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.
COMMUNICATION SKILLS
Ability to spot and present the most suitable empirical framework for the analysis based on the nature of the data at hand and effective communication of data analysis results, also by means of graphs and tables.
LEARNING SKILLS:
Ability to learn autonomously further data analysis techniques, in professional activities or subsequent studies, achieved through the analysis of econometric methods applied in
economics, finance and management.
The course lays the foundamentals for the statistical and econometric analysis of both sectional and time series data, highlighting the potential applications to economic and
financial research questions.
Contents cover the Linear Regression Model, in both simple and multiple specification, with its assumptions and properties. As for the time-series analysis, the course introduces
the notions of auto-correlation, heteroskedasticity and non-stationarity, analysing the consequences in terms of model estimation outcomes and illustrating how to properly deal
with those features in the data analysis.
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 area1: Information and data literacy; Competence area 2: Communication and collaboration;
Competence area 3: Digital content creation).
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.
COMMUNICATION SKILLS
Ability to spot and present the most suitable empirical framework for the analysis based on the nature of the data at hand and effective communication of data analysis results, also by means of graphs and tables.
LEARNING SKILLS:
Ability to learn autonomously further data analysis techniques, in professional activities or subsequent studies, achieved through the analysis of econometric methods applied in
economics, finance and management.
Prerequisites
Data Analysis and Descriptive Statistics, Probability and Inference. ANOVA
Program
Linear Regression Model, both simple and with multiple regressors
Assumptions and Diagnosis
Inference
Internal and External Validity
Regression Analysis of Economic Time Series Data
Dynamic Causal Effects
Assumptions and Diagnosis
Inference
Internal and External Validity
Regression Analysis of Economic Time Series Data
Dynamic Causal Effects
Books
James H. Stock and Mark W. Watson (fifth edition), Introduction to Econometrics
Teaching methods
During the whole duration of the course (6 weeks), there will be 3 weekly classes of 2 hours each, and 1 practice of 2 hours.
In all appointments, an active participation to the class will be strongly encouraged.
In all appointments, an active participation to the class will be strongly encouraged.
Exam Rules
The final written exam is a closed-book exam, consisting of both theoretical and empirical questions covering the entire program of the course. Questions can be both open and multiple choice and can feature graphs and estimation output, with the aim to evaluate the ability of the student to interpret the final results of a rea-world dataset.
Final evaluation ranges between 18 and 30. Scores lower than 18 will be recorded as Fail.
Final evaluation ranges between 18 and 30. Scores lower than 18 will be recorded as Fail.