Overview
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, moving average, exponential smoothing and decomposition. The study of applied work is emphasized in this non-specialist module.
Prerequisites
Working knowledge of statistical concepts up to the level of linear regression Everything on this checklist… [Please read this!]
Textbook
Hill, Griffiths & Lim (2011). Principles of Econometrics (4th ed). John Wiley & Sons. ISBN-10: 0470626739. ISBN-13: 978-0470626733.
Syllabus
Topic 1 Regression with Time Series Data – Stationary Variables (Lecture Notes Stata Codes and Outputs Data Sets)
1.1 Introduction to Time Series Analysis (Lecture Recording) 10:22
1.1 Introduction to STATA (Stata Demonstration) 5:00
1.2 Finite Distributed Lags (Lecture Recording 10:30 Stata Demonstration 14:44)
1.3 Serial Correlation (Lecture Recording 13:12 Stata Demonstration 33:32)
1.4 Estimation with Serially Correlated Errors (Lecture Recording 18:18 Stata Demonstration 14:01)
1.5 Autoregressive Distributed Lag Models (Lecture Recording 9:07 Stata Demonstration 13:50)
1.6 Forecasting (Lecture Recording 11:51 Exponential Smoothing Stata Demonstration 8:18)
1.7 Multiplier Analysis (Lecture Recording 6:22 Stata Demonstration 5:12)
Topic 2 Regression with Time Series Data – Nonstationary Variables (Lecture Notes Stata Codes and Outputs)
2.1 Stationary and Nonstationary Variables (Lecture Recording 14:24 Stata Demonstration 7:26)
2.2 Spurious Regressions (Lecture Recording 2:31 Stata Demonstration 3:39)
2.3 Unit Root Tests (Lecture Recording 9:07 Stata Demonstration 4:38)
2.4 Cointgration (Lecture Recording 14:25 Stata Demonstration 6:42)
Topic 3 Vector Error Correction and Vector Autoregressive Models (Lecture Notes Stata Codes and Outputs)
3.1 VEC models (Lecture Recording 11:20 Stata Demonstration 9:55)
3.2 VAR models (Lecture Recording 5:03 Stata Demonstration 13:22)
3.3 Impulse Responses and Variance Decomposition (Lecture Recording 21:14 Stata Demonstration 4:37)
Topic 4 Time-Varying Volatility and ARCH Models (Lecture Notes Stata Codes and Outputs)
4.1 Time-Varying Volatility (Lecture Recording 14:09 Stata Demonstration 4:02)
4.2 Testing, Estimating, and Forecasting (Lecture Recording 6:45 Stata Demonstration 6:23)
4.3 GARCH, T-ARCH, and GARCH-in-mean models (Lecture Recording 10:11 Stata Demonstration 6:40)