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## Syllabus

EN IT

### Updated A.Y. 2022-2023

Simple (revision) and multiple linear regression model
Assumptions and diagnosis
Inference
Internal and external validity
Regression analysis and forecasts of time-series
Dynamic causal effects

TEXTBOOK: Introduction to Econometrics, James H. Stock and Mark W. Watson (fifth edition)

LEARNING OUTCOMES:
The course lays the fundamentals 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, analyzing 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 microdata.

The course also develops digital competencies 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).

KNOWLEDGE AND UNDERSTANDING
Knowledge of data types and related univariate analysis techniques, including simple linear regression model, multiple linear regression model, and time-series models.

APPLYING KNOWLEDGE AND UNDERSTANDING
Ability to select appropriate data analysis methods and to analyze relationships among variables in economics, finance, and management.

MAKING JUDGMENTS:
Ability to collect, use and critically interpret 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, and 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.