The aim of this course is to enable students to understand the basic characteristics of time series data, analyze stationarity and unit root concepts, apply univariate and multivariate time series models, and make forecasts using economic and financial data. Within the scope of the course, students are expected to learn fundamental time series methods such as AR, MA, ARMA, ARIMA, VAR, cointegration, error correction models, causality tests, and volatility models. The course also aims to help students conduct empirical analysis using econometric software, interpret model results, and present findings in an academic report format.

This course covers the structure of time series data, trend, seasonality, stationarity, autocorrelation, unit root tests, AR, MA, ARMA and ARIMA models, model selection, forecasting, VAR models, Granger causality test, cointegration analysis, error correction models, ARCH/GARCH volatility models, and econometric software used in time series analysis. Throughout the course, students learn how to analyze economic and financial time series, select an appropriate econometric model, test model assumptions, interpret forecasting results, and prepare an empirical analysis report.