Data Analysis, Descriptive Statistics, Probability, and Inference.

The course introduces students to the quantitative evaluation of macro- and micro-economic data.

1. Simple Linear Regression: (i) regression models (assumptions and proofs); (ii) estimating procedure (assumptions and proofs); (iii) hypothesis testing, confidence intervals, and p-values; and (iv) diagnostic tests.

2. Multiple Linear Regression: (i) regression models (assumptions and proofs); (ii) asymptotic distributions; (iii) internal and external validity; (iv) hypothesis testing, confidence intervals, and p-values; (v) diagnostic tests; and (vi) joint hypothesis testing.

3. Some Non-linear Regression Models: (i) polynomial functions (assumptions and proofs); (ii) logarithmic functions (assumptions and proofs); (iii) interactions among causal variables; and (iv) interactions among causal and dummy variables.

4. Linear Time-series Regressions: (i) autoregressive models (assumptions and proofs); (ii) estimating procedure; (iii) diagnostic tests; and (iv) forecasting.

During the course, the statistical-econometric (open source) software used is going to be RStudio evaluating real case studies with macro- and micro-data.

(A); Stock, James H. and Watson, Mark W. (fourth edition), "Introduction to Econometrics", Pearson, Milan (2019), ISBN 978-1292264455. Chapters and related pages will be confirmed during the lessons.

(C); Wooldridge, Jeffrey M. (from fifth edition onwards), "Introductory Econometrics: A Modern Approach", The MIT Press, London (2010), ISBN 978-1111531041. Chapters and related pages will be suggested during the lessons.

(C); Tsay, Ruey S. (from second edition onwards), "Analysis of Financial Time Series", Wiley Series, Chicago (2010), ISBN 978-0471415442. Chapters and related pages will be suggested during the lessons.

• Lectures, exercises, applied exercises, and case studies based on real data with application in RStudio.

• • The final examination consists of a written exam, addressing either theoretical or empirical questions, and discussing estimation outputs and graphical representations.
  • The evaluation criteria will be divided into: knowledge and understanding of the topics covered in the course (50%) and ability to apply the appropriate tools and completeness of the resolutions submitted (50%).
  • Students attending the course are requested to solve a Project Work analysing a real case-study by using the statistical-econometric software RStudio. The Project Work will be evaluated until 2 points to be added to the exam grade.
  • If the student does NOT develop the Project Work, he/she could (on availability of the Teacher) be authorized to take the final written test as well, but with a PENALTY of -3 on the final grade.
  • The final grade is then computed by adding up the result of the written examination with the result obtained in the Project Work (from -1 to 2). This latter is limited to the exam held - with any outcome - in one of the appeals of the first exam session.

English