Title: Quantitative Topics in Economics: Time Series Analysis with Linear Methods  Semester: Winter (1^{st}) 
Tutors: Dimitrios Kugiumtzis, Professor &
Catherine Kyrtsou, Professor 
Course Outline:

 Introduction to time series analysis, stationarity and autocorrelation
 Examples of realworld time series
 Stationarity and autocorrelation
 Some fundamental stochastic processes
 Sample autocorrelation
 Nonstationary time series, unitroot test and test for independence
 Variance stabilization
 Removal of trend and seasonality / periodicity
 Unitroot tests
 Test for independence
 Exercises
 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 nonstationary time series
 Exercises
 Time series prediction
 Simple prediction techniques
 Prediction of stationary time series using linear models
 Prediction of nonstationary time series
 Exercises
 Stationary multivariate time series and models
 Crosscorrelation
 Vector autoregressive models
 Granger causality
 Exercises
 Nonstationary multivariate time series and models
 Cointegration and cointegration test
 Vector autoregressive models with integrated variables
 Exercises
 Equilibrium Economic Systems: The concept of efficiency
 Market efficiency
 Characteristics of equilibrium regimes in economics
 Applications to economic data (Eviews)
 Outofequilibrium Economic Systems: The concept of heterogeneity
 Heterogeneous markets
 Characteristics of disequilibrium regimes in economics
 Investors’ types
 Understanding investors’ profiles
 Applications to economic data (Eviews)
 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)
 Complexity in Economics
 The concept of complexity
 Synergy between complexity and economic theory
 Complexity and economic crises
 Applications to economic data (Eviews)
 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)
 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 postgraduate program “Statistics and Modeling”, Department of Mathematics, Aristotle University of Thessaloniki (in Greek)
5. Bodie, B., Kane, A., and Marcus, A. (2014), Investments, McGrawHill Education.
6. Cuthbertson, K., and Nitzsche, D. (2004), Quantitative Financial Economics, Wiley.
7. Enders, W. (2014), Applied Econometric Time Series, Wiley.
8. Warneryd, KE. (2001), StockMarkets Psychology, Edward Elgar Publishing.