Description
Summary: This part of the course discusses estimation and inference in econometric models. The 铿乶al couple of lectures will introduce basic elements of time series analysis.
Prerequisites: The course assumes knowledge of Econometrics at the MSc level. The following concepts are taken for granted: random variables, distribution functions (marginal, joint, conditional), transformations of random variables, independence, expectations, conditional expectations, moments, covariance and correlation, (multivariate) normal distribution. To review these topics, see the 铿乺st few chapters of Casella and Berger (2001). Importantly, I will also assume knowledge of most of the 铿乺st chapter of the recommended textbook (chapters 1.1鈥1.6 of Hayashi (2000)), which includes: the classical linear regression model, OLS estimation, 铿乶ite-sample properties of OLS, hypothesis testing under normality, GLS estimation. Part of this material will be covered by problem sets, but it is a good idea to read up on it beforehand.
Module deliveries for 2024/25 academic year
Last updated
This module description was last updated on 19th August 2024.
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