Course: Topics in applied time-series analysis: Models, seasonal adjustment, and forecasting

Professor: Andrei Kostyrka

ECTS: 1

Aims:

The goal of this course is to get Ph.D. students acquainted with several applied time-series methods that are actively used at major economic institutions (Eurostat, Statec, central banks etc.). The main focus of the course will be on the seasonal features of macroeconomic time series (monthly and quarterly), on seasonal adjustment, and on short-term modelling and forecasting. Computations are done in free and open-source software: R and JDemetra+.

Learning Objectives:

Upon successful completion of this course, students will be able to:

• Diagnose macroeconomic series and use data-driven methods to detect structural breaks and calendar effects;
• Filter out the seasonal component of time series by parametric and semi-parametric methods and to evaluate the quality of seasonal adjustment;
• Apply state-of-the-art econometric methods to produce forecasts or to reconstruct partially incomplete macroeconomic data sets with R;
• Carry out Eurostat-compliant seasonal adjustment in JDemetra+ and be familiar with its most recent guidelines.