ECO665 Applied Time Series Analysis [Spring 2021]

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ECO665 Applied Time Series Analysis

Aims and Objectives

This course aims to introduce time series analysis and its applications in economics/finance. At the end of the course, the students shall be able to create time series models for real world time series data. The main software used in this course for statistical programming is R. Students shall be able to use R and its related packages. The content of the course is dynamic and changing in every year. This year content includes (but not limited):

• Introduction
  
  
• R
  • R and RStudio Installation & Resources
    
  • R Basics
  • Date in R
  • Packages for TS
  • Rmarkdown for HW Assignments
    
  • An Example: IBM Stock Price Data

• Time series Concept: Discrete-continuous time series, domains, probability-time-frequency domain

• Time series and causality: Why Econometrics is different. This is in-class discussion. But refer to Judea Pearl home page for some discussions. Specifically read The Art and Science of Cause and Effect.

• Exploratory Time Series analysis

  • Graphical Methods
    
  • Numerical Methods
    
    

• Univariate Time Series Models

  • Stationary Time Series Models (AR, MA, ARMA Processes)
    
  • Non-Stationary Time Series Models (ARIMA Processes)
    
    

• Intervention Analysis, Transfer Function Models
  
  

• Outliers, Change Points and Anomaly Detection
  

• Volatility Modelling, (ARCH, GARCH, DCC-GARCH Volatility Modeling)
  

• Multi-Variate Time Series Models

  • VAR Models
  • Causality (Granger): Discussed in-class
    
  • Cointegration: Discussed in-class
    
    

• Linear Panel Models, (Intro to Panel Data Models)
  
  

References and Suggested Readings

• Jonathan D. Cryer and Kung-Sik Chan, “Time Series Analysis with Applications in R”, second edition, Springer

• Shumway, Robert H., Stoffer, David S, “Time Series Analysis and Its Applications, With R Examples”, third edition, Springer

• Tsay, R. S. Analysis of financial time series 3rd Edition. John Wiley & Sons.

Data Sources

There are several Economic/Finance data sources available. Here you can find some examples

Data Cleaning Preprocessing

Data rarely come clean. There may be a need for cleaning them. For example, time series data may contain outliers, missing values and errors. Understanding the properties of data and cleaning them probably the very first step in time series analysis.

Please read related part of Exploratory Time Series Analysis

Modelling Stationary Stochastic Time Series

You may find many examples in our textbooks. Cryer and Chen’s chapter 4, Shumway and Stoffer’s chapter 3.1 and 3.2 and Tsay’s book chapter 2 should be read first. Also R. Hyndman’s Forecastiong: Principles and Practice online book’s chapter 8 is a good read for introduction of univariate time series modelling.

Check the in-class discussions of Ar-Ma-ARMA Modeling

Modelling Non-Stationary Stochastic Time Series

• Classical Box-Jenkings Modelling: Please do refer to Forecastiong: Principles and Practice, R. J. Hyndman and G. Athanasopoulos, open access textbook chapter 8, Arima Models. and ARIMA modeling.

Transfer Function Models

Intervention Analysis (an intro to transfer function models and the ARMAX models)

Volatility Modeling

ARCH, GARCH, DCC-GARCH Volatility Modeling

Volatility modelling is a type of modelling frequently used in both economic and financial applications. Here we restrict ourselves discrete time modelling practice of ARCH-GARCH type of models. The diffuculty for these type of models lies on the exploding number of different type of GARCH modelling. We will cover standard model and a few more examples of different modelling. Cryer and Chan: Chapter 12, Tsay: Chapter 3, Shumway and Stoffer: Chapter 5 are the texbook parts regarding ARCH-GARCH modelling.