Course Overview:
This short professional online course is designed for academics, PhD students, and professors who wish to deepen their understanding of time series analysis in macroeconomics. The course provides comprehensive training on the theoretical foundations, practical implementation, and policy applications of time series analysis in macroeconomic research.
Course Objectives:
Understand the principles and techniques of time series analysis in macroeconomics.
Develop skills to model, estimate, and forecast macroeconomic time series data.
Apply time series methods to analyze and interpret macroeconomic phenomena.
Utilize software tools such as R, EViews, Stata, and MATLAB for time series analysis.
Target Audience:
Academics in economics and related fields.
PhD students specializing in macroeconomics, econometrics, and time series analysis.
Professors and researchers aiming to enhance their analytical skills in time series modeling.
Course Structure: The course is structured into four intensive modules, each featuring lectures, readings, assignments, and practical exercises. Participants will engage with video lectures, interactive coding sessions, and peer discussions.
Week 1: Fundamentals of Time Series Analysis
Basics of time series data and its characteristics.
Stationarity, trend, seasonality, and autocorrelation.
Week 2: Time Series Models and Estimation
Autoregressive (AR), Moving Average (MA), and ARMA models.
Estimation techniques for time series models.
Readings:
"Time Series Analysis and Its Applications" by Robert H. Shumway and David S. Stoffer.
"Introduction to Time Series and Forecasting" by Peter J. Brockwell and Richard A. Davis.
Week 3: ARIMA and SARIMA Models
Building and estimating ARIMA models.
Seasonal ARIMA (SARIMA) models for seasonal data.
Week 4: Vector Autoregressions (VAR)
Introduction to VAR models and their applications.
Impulse response functions and forecast error variance decomposition.
Readings:
"Time Series Analysis" by James D. Hamilton.
"Applied Econometric Time Series" by Walter Enders.
Week 5: Cointegration and Long-Run Relationships
Concept of cointegration and the Engle-Granger method.
Johansen cointegration test and its applications.
Week 6: Error Correction Models and Structural Breaks
Error correction models (ECM) for short-term adjustments.
Detecting and modeling structural breaks in time series data.
Readings:
"Econometric Analysis of Time Series" by Andrew C. Harvey.
Selected articles from the Journal of Econometrics.
Week 7: Time Series Forecasting in Macroeconomics
Techniques for forecasting macroeconomic variables.
Evaluating forecast accuracy and performance.
Week 8: Real-World Applications and Case Studies
Case studies on time series analysis in macroeconomic policy.
Presentation and discussion of participant projects.
Readings:
"Forecasting Economic Time Series" by Michael P. Clements and David F. Hendry.
Relevant policy papers and case studies from central banks and international organizations.
Assignments:
Weekly problem sets and practical exercises.
Mid-term project involving the estimation and forecasting of a macroeconomic time series.
Final Project:
Comprehensive time series analysis project on a macroeconomic topic of choice.
Presentation and peer review of the final project.
Certification:
Participants who complete all modules, assignments, and the final project will receive a certificate of completion.
Course Delivery:
The course will be delivered through a combination of pre-recorded video lectures, live Q&A sessions, interactive tutorials, and discussion forums.
All course materials, including readings, software guides, and lecture slides, will be available online.
Instructor:
The course will be led by a team of experienced economists and econometricians with extensive expertise in time series analysis and macroeconomics.
Enrollment:
Participants can enroll through the university’s online learning platform.
Enrollment will be open to individuals with a foundational knowledge in macroeconomics and econometrics.
By the end of this course, participants will have a robust understanding of time series analysis techniques and be equipped with the skills to apply these methods to empirical macroeconomic research, policy analysis, and forecasting.