1. Knowledge and understanding: The course aims at presenting the principles of econometrics: estimators and their properties; hypothesis testing.
2. Applying knowledge and understanding: The student has to be able to apply the theoretical knowledge acquired in order to analyse the econometric evidence provided in scientific articles, and in order to build a (simple) econometric exercise (multiple regression analysis).
3. Making judgements: The student will be able to understand meaning, role and limits of an econometric model.
4. Communication skills: During the course the student has to improve and develop the knowledge of a technical and economic language; the student has be able to explain (to both experts and laymen) meaning and characteristics of an econometric model.
5. Learning skills: The student will be able to understand which theoretical concept is appropriate to deal with specific problems in econometric modelling.
Lectures (70%); guided exercises about econometric estimation (using an econometric software) (30%).
(1) Introduction to econometrics and its role in the scientific character of economics; (2) The simple linear regression; (3) Interval estimation and hypothesis testing; (4) Multiple regression and OLS; (5) Regressor endogeneity and IV estimator; (6) GLS estimation; (7) Stationary and non-stationary time series (ARMA and ARIMA models); (8) Dynamic specification; (9) VAR and VECM; (10) Qualitative and Limited Dependent Variable Models; (11) econometirc models with financial data with high frequency. Applications - building and evaluating an econometric model: (a) Empirical exercises from the textbook and from the Instructor; (b) critical reading of econometric evidence provided in scientific articles; (c) individual construction and validation of an econometric model.
1: C. HILL - W. E. GRIFFITHS - G.C. LIM, Principles of Econometrics, (Last edition)
As an alternative: . J.H. Stock – M. W. Watson, Introduction to Econometrics, Pearson
2: "A guide for GRETL" (freely downloadable from the web.