Instructor: Gabriele Fiorentini
Time-Series Models, Difference Equations and Their Solutions, Lag Operators. Stochastic Difference Equation Models, ARMA Models, Stationarity, Stationarity Restrictions for an ARMA (p,q) Model , The Autocorrelation Function, The Partial Autocorrelation Function, Sample Autocorrelations of Stationary Series, Box-Jenkins Model Selection, Properties of Forecasts, Seasonality, Structural Change, Combining Forecasts. Deterministic and Stochastic Trends, Removing the Trend, Unit Roots and Regression Residuals, The Monte Carlo Method, Dickey-Fuller Tests and extensions, Power and the Deterministic Regressors, Panel Unit Root Tests, Trends and Univariate Decompositions, Intervention Analysis, ADLs and Transfer Functions, Limits to Structural Multivariate Estimation, Introduction to VAR Analysis, Estimation and Identifcation, The Impulse Response Function, Structural VARs, Examples of Structural Decompositions, Overidentifed Systems, The Blanchard-Quah Decomposition. Linear Combinations of Integrated Variables, Cointegration and Common Trends, Cointegration and Error Correction, Testing for Cointegration: The Engle-Granger Methodology, Cointegration and Purchasing Power Parity, Characteristic Roots, Rank, and Cointegration.
Instructor: Giorgio Calzolari
Overview of the linear regression model and ordinary least squares. Estimation (OLS): algebraic properties, statistical properties of OLS, Gauss Markov theorem, forecast errors, distribution of linear and quadratic forms, linear restrictions, restricted least squares, t test, F test, structural change, specification errors, heteroskedasticity. Linear models for panel data: fixed effects versus random effects. Truncation and censored regression model (Tobit). Discrete choice models: Logit, Probit