Description

Time Series Analysis has wide applicability in economic and financial fields but also to geophysics, oceanography, atmospheric science, astronomy, engineering, among many other fields of practice. This course consists of a hands on introduction to time series analysis using current methodology through R with applications to finance and environmental science. This course will cover  the standard time series analysis topics such as modeling time series using regression analysis, univariate ARMA/ARIMA modelling, (G)ARCH modeling, Vector Autoregressive (VAR) model along with forecasting, model identification and diagnostics.

Prerequisites

An undergraduate backround in regression analysis, linear algebra and statistical inference is expected.

Basic references

• Time series analysis and its applications by RH Shumway and DS Stoffer, Springer, 2011.
• Handbook of financial time series by TG Andersen, RA Davis, J-P Kresiß, T Mikosch (eds), Springer, 2009.
• Statistics for spatio-temporal data by N Cressie and CK Wikle, Wiley, 2011.
• Workshop: Applied spatial statistics in R by YV Zhukov, http://www.people.fas.harvard.edu/∼zhukov/spatial.html.
• Analysis of financial time series (3rd ed) by R Tsay, Wiley, 2010.
• Hidden Markov models for time series by W Zucchini and I MacDonald, CRC Press, 2009.