Indicative Module Content:

The Detailed Syllabus:

• Introduction and basic concepts
Expected value and variance of a time series.
Autocovariance and autocorrelation (sample and theoretical).
Stationarity: weak and strict.
Backshift operator.

• Box-Jenkins Modelling: ARIMA Models
ARIMA models
Polynomials in B and root of the equations
Yule-Walker equations
Solving recurrent difference equations

• Model Identification
The sample autocorrelation function (ACF)
Convergence of the sample ACF
ACF of ARIMA models
Partial autocorrelation function (PACF)
Durbin-Levinson equations
PACF of ARIMA models
Overdifferencing

• Model Estimation
ARIMA model with drift or trend
Estimating the trend.
Estimation of centered ARIMA
Method of moments
Least squares
Maximum likelihood estimation

• Model Bootstrapping
Bootstrapping for ARIMA models

• Diagnostics
Residuals
qq-plot of residuals
Shapiro-Wilk test
ACF and Ljung-Box-Pierce tests

• Forecasting
Conditional expectations
Minimum mean square error forecast
Forecasting error
Confidence intervals

• Time Series Decomposition
Components of a time series
Seasonality and trend
Estimate the trend and seasonality
Moving average
Additive decomposition
Multiplicative decomposition
 
• Non-stationary Time Series
Difference-stationary series
Trend-stationary series
Dickey-Fuller unit root tests
Augmented Dickey-Fuller test (ADF)

• Heteroscedasticity
Heteroskedasticity versus Homoskedasticity
McLeod-Li test to check cluster volatility for ARMA
Autoregressive Conditionally Heteroscedastic models (ARCH)
Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH)
ARMA-GARCH Models

• Spurious Regressions
Cross-Correlation function (CCF)
Sample cross-correlation function
Causes of spurious regressions
Whitening (prewhitening)
How to recognize spurious correlations?

• Multivariate Time Series
Vector AutoRegressive (VAR) models
Companion Form of VAR models
Stability for VAR model
Cointegrated time series
Error Correction Model (ECM)
Engle-Granger method

Student Effort Type	Hours
Lectures	0
Total	0

Not applicable to this module.

Required:
BDIC1034J - College English 1, BDIC1035J - College English 2, BDIC1036J - College English 3, BDIC1037J - College English 4, BDIC1047J - English for Uni Studies BDIC, BDIC1048J - English Gen Acad Purposes BDIC, BDIC2007J - English for Spec Acad Purposes, BDIC2015J - Acad Wrt & Comm Skills

Incompatibles:
ECON30540 - Advd Econometrics: Time Series


 

Assessment Strategy  

Description	Timing		Component Scale		% of Final Grade
Examination: Assessment will be based on the midterm and final exams.	Unspecified	No	Alternative linear conversion grade scale 40%	No	100

 

Remediation Type	Remediation Timing
In-Module Resit	Prior to relevant Programme Exam Board

• Feedback individually to students, post-assessment

Not yet recorded.

Robert H. Shumway & David S. Stoffer: "Time Series Analysis and Its Applications: With R Examples", Springer, 4th edition
(2016).