Quantitative Topics in Economics: Time Series Analysis with Linear Methods

Course Outline:

1. 1. Introduction to time series analysis, stationarity and autocorrelation
   • Examples of real-world time series
   • Stationarity and autocorrelation
   • Some fundamental stochastic processes
   • Sample autocorrelation
   2. Non-stationary time series, unit-root test and test for independence
      • Variance stabilization
      • Removal of trend and seasonality / periodicity
      • Unit-root tests
      • Test for independence
      • Exercises
   3. Linear stochastic processes and models
      • Linear stochastic processes, stationarity and reversibility
      • Parameter estimation and estimation of the order of autoregressive models (AR), moving average models (MA) and autoregressive moving average models (ARMA)
      • ARIΜA models for non-stationary time series
      • Exercises
   4. Time series prediction
      • Simple prediction techniques
      • Prediction of stationary time series using linear models
      • Prediction of non-stationary time series
      • Exercises
   5. Stationary multi-variate time series and models
   • Cross-correlation
   • Vector autoregressive models
   • Granger causality
   • Exercises
   6. Non-stationary multi-variate time series and models
   • Co-integration and co-integration test
   • Vector autoregressive models with integrated variables
   • Exercises
   7. Equilibrium Economic Systems: The concept of efficiency
   • Market efficiency
   • Characteristics of equilibrium regimes in economics
   • Applications to economic data (Eviews)
   8. Out-of-equilibrium Economic Systems: The concept of heterogeneity
   • Heterogeneous markets
   • Characteristics of disequilibrium regimes in economics
   • Investors’ types
   • Understanding investors’ profiles
   • Applications to economic data (Eviews)
   9. Information Theory
   • The meaning of information
   • Specific features of information signals in modern markets
   • Manipulation of information and impact on prices
   • Applications to economic data (Eviews)
   10. Complexity in Economics
   • The concept of complexity
   • Synergy between complexity and economic theory
   • Complexity and economic crises
   • Applications to economic data (Eviews)
   11. Interdependence in Economics
   • The concept of interdependence in macroeconomic systems
   • The concept of interdependence in financial markets
   • Interdependence measures
   • Interdependence and risk
   • Applications to economic data (Eviews)
   12. Systemic Risk
   • Dimensions of risk in real economic environments
   • The concept of contagion
   • Applications to economic data (Eviews)

    

Aim:

Familiarisation with the basic tools of modern economic analysis.

On completion of this module, students are expected to be able to:

• to understand and analyze the conditions under which an economic system is being destabilised or is staying in equilibrium
• to recognize the causes of the ineffectiveness of economic policies and product management in financial markets
• to decode the informational content of the disturbances that affect the evolution of prices in the markets
• to quantify the result of behavioral anomalies in markets.

Suggestions for further reading:

1. Neusser, K. (2016), Time Series Econometrics, Springer.

2. Verbeek, M. (2004), A Guide to Modern Econometrics, 4th Edition, Wiley.

3. Mills, T.C. and Markellos, R.N. (2008), The Econometric Modelling of Financial Time Series, 3nd edition, Cambridge Press

4. Kugiumtzis D. (2016). Time Series Analysis, Notes for the course “Time Series Analysis” of the post-graduate program “Statistics and Modeling”, Department of Mathematics, Aristotle University of Thessaloniki (in Greek)

5. Bodie, B., Kane, A., and Marcus, A. (2014), Investments, McGraw-Hill Education.

6. Cuthbertson, K., and Nitzsche, D. (2004), Quantitative Financial Economics, Wiley.

7. Enders, W. (2014), Applied Econometric Time Series, Wiley.

8. Warneryd, K-E. (2001), Stock-Markets Psychology, Edward Elgar Publishing.