Statistical Models for Forecasting

Basic information

Workload: 

45 hours

Prerequisite: 

 Mathematical Statistics

Syllabus: 

State Space Models, Bayesian Belief Update, Bayesian Dynamic Models, ARIMA Models, GARCH Models, Autoregressive Vector Models (VAR), Impulse Response Functions, Unit Root Processes, Cointegration, Principal Component Analysis, Factor Models, Correlation Dynamic Conditional Models (DCC), Copulas

Bibliography

Mandatory: 

·       Morettin, Pedro Alberto. Econometria financeira: um curso em séries temporais financeiras. Edgard Blücher, 2008.
·       Tsay, Ruey S. Multivariate Time Series Analysis: with R and financial applications. John Wiley & Sons, 2013.
·       Hyndman, Rob J., and George Athanasopoulos. Forecasting: principles and practice. OTexts, 2014.

Complementary: 

·       Efron, Bradley, and Trevor Hastie. Computer age statistical inference. Vol. 5. Cambridge University Press, 2016.
·       Brockwell, Peter J., and Richard A. Davis. Introduction to time series and forecasting. springer, 2016.
·       Randal Douc, Eric Moulines, and David Stoffer. Nonlinear time series: theory, methods and applications with R examples. CRC Press, 2014.
·       Hamilton, James Douglas. Time series analysis. Vol. 2. Princeton: Princeton university press, 1994.
·       Harrison, Jeff, and Mike West. Bayesian forecasting and dynamic models. Springer, 1999.