Login
Student authentication

Is it the first time you are entering this system?
Use the following link to activate your id and create your password.
»  Create / Recover Password

TIME SERIES AND ECONOMETRICS

Program

Updated A.Y. 2019-2020

Prerequisites

Linear algebra (operations with matrices and vectors, trace of a matrix, properties of symmetric matrices, quadratic forms, eigenvectors, eigenvalues, diagonalization).

Calculus.  

Syllabus

The following syllabus is to be considered temporary and may change in the future.

Econometrics 

Simple linear regression model:

  • OLS estimators: derivation through first order conditions. 
  • Definition and interpretation of the coefficient of determination.
  • Unbiasedness of OLS estiamtors: theory and practice (with Matlab).
  • Conditional variance of OLS estiamtors: theory and practice (with Matlab). 
  • Unbiased estimator of error variance. 
  • Statistical inference: hypothesis testing and t-statistic. 
  • Statistical inference: the Capital Asset Pricing Model and the beta of a stock. 

Multiple linear regression model: 

  • Recap of matrix algebra and gradient of a function. 
  • OLS estimators: derivation through first order conditions.
  • Unbiasedness of OLS estiamtors. 
  • Conditional variance-covariance matrix of OLS estiamtors.
  • Unbiased estimator of error variance.
  • Multicollinearity.
  • Blueness of the OLS estimator: the Gauss-Markov theorem. 
  • Multiple hypothesis testing. 
  • Maximum Likelihood Estimator.
  • Model comparison. 
  • OLS asymptotics. 
  • OLS diagnostics. 

Bibliography

Wooldridge J. M.  (2016). Introductory Econometrics: A Modern Approach.

Brooks C. (2014). Introductory Econometrics for Finance. 

 

Time Series 

  • Introductory concepts: trends, seasonality and returns.
  • Strongly stationarity: definition and main properties.
  • IID noise. 
  • Weekly stationarity: definition and main properties.
  • Ergodicity
  • AR, MA and ARMA processes. 
  • Vector Auto-Regressive processes: estimation and identification. 
  • Cointegration. 

 

Bibliography

Wei (2006). Time Series Analysis: Univariate and Multivariate Methods. Addison-Wesley.

Brockwell P.J. and Davis R.A. (2002). Introduction to Time Series and Forecasting, Springer.

Brockwell P.J. and Davis R.A. (1991). Time Series: Theory and Methods. Springer.