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Wed 13 Feb 2019
09:00 - 18:00
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Provided by: Social Sciences Research Methods Programme


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Time Series Analysis (Intensive)

Wed 13 Feb 2019

Description

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

Number of sessions: 2

# Date Time Venue Trainer
1 Wed 13 Feb   09:00 - 13:00 09:00 - 13:00 8 Mill Lane, Lecture Room 1 map Dr L. Li
2 Wed 13 Feb   14:00 - 18:00 14:00 - 18:00 University Information Services, Titan Teaching Room 1, New Museums Site map Dr L. Li
Assessment

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

Textbook

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

Software

Stata

How to Book

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Duration

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


Booking / availability