ECONOMETRICS

The course aims to handle some aspects of the econometric methods with respect to the regression models, both uni- and multi-equational like the models of simultaneous equations and the vector autoregressive models (VAR). Consequently, the purpose is to prepare the student to use the basic econometric tools for the measurement, interpretation and forecast of the economic and financial phenomena. The course is well equipped with econometric practice.

At the end of the course the students are able to know the relevant part of the econometric theory that is essential for the interpretation of the results produced by an econometric software. In particular, they will acquire skills in the treatment of macroeconomic and financial data for the specification, estimation and forecasting with regression models. The preparation is completed with the analysis of current aspects of the real and financial economy, using time series downloaded from available databases.

Elements of matrix algebra, theory of random variables, elements of statistical inference: Estimation and hypothesis testing.

1. Introduction to the linear regression model
1.1. Introduction
1.2. Bivariate regression model
1.3. Multivariate regression model
1.4. Probabilistic interpretation of the regression
1.5. Properties of the estimators: examples
1.6. Regression with linear constraints
2. Asymptotic properties of the estimators
2.1. Stochastic convergences
2.2. Asymptotic properties OLS estimator
3. Univariate stochastic and multivariate stationary processes
3.1. Univariate stochastic processes
3.2. Multivariate stochastic processes
3.3. Wold decomposition theorem and general linear processes (L)
3.4. Dynamic properties
3.5. Forecasting
4. Non-stationary stochastic processes
4.1. Processes with unity roots and spurious regression
4.2. Trend Stationary processes (TS) and Differences Stationary (DS)
4.3. Some examples of estimation of non-stationary economic series
5. Specification of the regression model
5.1. Inclusion of irrelevant variables and exclusion of relevant variables
5.2. Specification strategies
5.3. Selection of regressors
6 Specification strategies with integrated processes
6.1. Cointegrated processes and ECM representation
6.2. Simulation of ECM model
6.3. Generalization of the representation of ECM model
6.4. Specification strategies in the presence of regressors I (1) and I (0)
6.5. Simulation, estimation and forecasting of ECM model
7. Multi-equation models
8. Identification and Information (L)
9. Exogenous and incomplete systems of equations (L)
9.1. Exogeneity test (L)
10. Outline of Bayesian inference (L)
11. Outline of decision theory (L)
12. Variable transformations
13. Example of econometric project on actual economic data

NB: The chapters / paragraphs marked with (L) are optional or read-only

References:
A) Hamilton J.(1995), Econometria delle serie storiche, Trad. B. Sitzia, Monduzzi, Milano
B) Cappuccio N. e R. Orsi (2005), Econometria, Il Mulino
C) Peracchi F. (1995), Econometria, McGraw-Hill Libri Italia
D) Verbeck M. (2006), Econometria, Zanichelli

The exam is individual and consists in:
a) presentation of a uniequational multivariate regression model on current data of the real or financial economy;
b) exposition and discussion of some topics of econometric theory

Lectures, tutorial exercises, practical application on economic and/or financial data using econometric software

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