Anno Accademico 2020/2021
- Docente: Andrea Carriero
- Crediti formativi: 6
- SSD: SECS-P/05
- Lingua di insegnamento: Inglese
- Modalità didattica: Convenzionale - Lezioni in presenza
- Campus: Bologna
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Corso:
Laurea in
Scienze statistiche (cod. 8873)
Valido anche per Laurea Magistrale in Statistical sciences (cod. 9222)
Conoscenze e abilità da conseguire
By the end of the course the student should be familiar with the theory and practice of single-equation linear econometric modelling. In particular, the student should be able to: - specify and estimate linear, single-equation econometric models with stochastic and possibly endogenous regressors; - derive and employ the asymptotic properties of linear method-of moments parameter estimators (OLS and IV) in these models; - perform a specification analysis of these models, - perform asymptotically valid inference based on these models.
Contenuti
- The Classical Linear Regression Model. Derivation of Ordinary Least Squares estimator (OLS). Decomposition of variance, R-squared.
- Small sample properties of the OLS estimator. Gauss-Markov Theorem
- Partitioned Regression, redundant/omitted variables, bias-variance trade-off, Frisch Waugh Theorem
- Inference. Tests of simple and joint hypothesis. Restricted Least Squares (RLS).
- Heteroscedasticity and autocorrelation. Generalised Linear Regression Model. Generalised Least squares Estimator (GLS), Feasible GLS (FGLS), HAC estimators.
- Stochastic regressors. Endogeneity. Large sample properties of OLS estimator.
- Instrumental Variables estimator (IV). Generalised IV (GIVE) and Two-Stage Least Squares estimator (TSLS).
- Maximum Likelihood Estimation (ML).
- Bayesian analysis of the linear regression mode
Testi/Bibliografia
Greene “Econometrics Analysis”, Pearson – any edition
Hansen “Econometrics” – manuscript, any editionModalità di verifica e valutazione dell'apprendimento
Written examination.
Orario di ricevimento
Consulta il sito web di Andrea Carriero