Time Series Analysis

This module introduces the time series techniques relevant to forecasting in social science research and computer implementation of the methods. Background in basic statistical theory and regression methods is assumed. Topics covered include time series regression, Vector Error Correction and Vector Autoregressive Models, Time-varying Volatility, and ARCH models. The study of applied work is emphasized in this non-specialist module. Topics include:

• Introduction to Time Series: Time series and cross-sectional data; Components of a time series, Forecasting methods overview; Measuring forecasting accuracy, Choosing a forecasting technique
• Time Series Regression; Modelling linear and nonlinear trend; Detecting autocorrelation; Modelling seasonal variation by using dummy variables
• Stationarity; Unit Root test; Cointegration
• Vector Error Correlation and Vector Autoregressive models; Impulse responses and variance decompositions
• Time-varying volatility and ARCH models; GARCH models

• Postgraduate students and staff
• Further details regarding eligibility criteria are available here

• A background in basic statistical theory and regression methods
• A working knowledge of statistical concepts up to the level of Linear Regression

There may be an online open-book test at the end of the module; for most students, the test is not compulsory.

Hill, Griffiths & Lim (2011). Principles of Econometrics (4th ed). John Wiley & Sons. ISBN-10: 0470626739. ISBN-13: 978-0470626733.

Stata

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8 hours - A morning lecture and an afternoon lab session
This is an intensive, one-day module