Last update: PhDr. Jaromír Baxa, Ph.D. (07.02.2022)
Students aiming for a career in central banks, academia, or international institutions will learn methods that are necessary to understand, replicate and conduct empirical research in macroeconomics.
The first part of the course covers modelling univariate time series (stationary and nonstationary models, spectral analysis, regime-shift models). The second part of the semester is devoted to multivariate models, forecasting, and identification of causal relationships in macroeconomics. The recently developed approaches to identification such as external instruments in VAR or high-frequency identification are covered as well.
Our course participants apply all covered methods in regular problem sets that are based on replications of academic papers. These problem sets are presented and discussed in the seminars.
Problem sets shall be prepared in R and delivered as Jupyter notebooks, sample R-codes are provided.
Last update: PhDr. Jaromír Baxa, Ph.D. (15.02.2023)
Students aiming for a career in central banks, academia or international institutions will learn methods that are necessary to understand, replicate and conduct empirical research in macroeconomics.
The first part of the course covers modelling univariate time series (stationary and nonstationary models, spectral analysis, regime-shift models). The second part of the semester is devoted to multivariate models, forecasting, and identification of causal relationships in macroeconomics. The recently developed approaches to identification such as external instruments in VAR or high frequency identification are covered as well.
Our course participants apply all covered methods in regular problem sets that are based on replications of academic papers. These problem sets are presented and discussed in the seminars.
Problem sets shall be prepared in R and delivered as Jupyter notebooks, sample R-codes are provided.
Course completion requirements -
Last update: PhDr. Jaromír Baxa, Ph.D. (15.02.2023)
Assignment: Final paper
Choose one country for which you will estimate the propagation of a shock of your interest and forecast the GDP growth and inflation. To do that, you will utilize the methods covered in the course and explore their properties, forecast performance, and robustness.
The final paper will be prepared throughout the whole semester, with three intermediate deadlines:
after lecture 4: ARIMA model of inflation and GDP, estimation of potential structural breaks, estimation of spectra of both series, forecast 1 period and 1 year ahead; evaluation of a cyclical position of the economy
after lecture 8: VAR I - short-run restrictions, sign restrictions + VAR forecasts.
after lecture 11: VAR II - local projections, Bayesian VAR + BVAR forecasts.
The outcomes of these intermediate stages will be presented and discussed during the seminars.
Grades: Intermediate stages and presentations - 20 points each (60 in total), final paper + presentation, and participation at the workshop 40 points. The final paper and participation at the workshop are necessary conditions to pass the course, even if the sum of intermediate points exceeds 50.5
Last update: PhDr. Jaromír Baxa, Ph.D. (15.02.2023)
Assignment: Final paper
Choose one country for which you will estimate the propagation of a shock of your interest and forecast the GDP growth and inflation. To do that, you will utilize the methods covered in the course and explore their properties, forecast performance, and robustness.
The final paper will be prepared throughout the whole semester, with three intermediate deadlines:
after lecture 4: ARIMA model of inflation and GDP, estimation of potential structural breaks, estimation of spectra of both series, forecast 1 period and 1 year ahead; evaluation of a cyclical position of the economy
after lecture 8: VAR I - short-run restrictions, sign restrictions + VAR forecasts.
after lecture 11: VAR II - local projections, Bayesian VAR + BVAR forecasts.
The outcomes of these intermediate stages will be presented and discussed during the seminars.
Grades: Intermediate stages and presentations - 20 points each (60 in total), final paper + presentation, and participation at the workshop 40 points. The final paper and participation at the workshop are necessary conditions to pass the course, even if the sum of intermediate points exceeds 50.5
Last update: PhDr. Jaromír Baxa, Ph.D. (07.02.2022)
We provide most of the necessary information in our presentations and in sample codes. If needed, we encourage students to consult in the following textbooks and in articles mentioned in the syllabus.
Enders, W.: Applied Econometric Time Series, 3rd ed., Wiley, 2009
Lütkepohl, H.: New Introduction to Multiple Time Series Analysis. Springer, 2005.
Kočenda, E., Černý, A.: Elements of Time Series Econometrics: An Applied Approach, Karolinum 2007
Last update: PhDr. Jaromír Baxa, Ph.D. (07.02.2022)
We provide most of the necessary information in our presentations and in sample codes. If needed, we encourage students to consult in the following textbooks and in articles mentioned in the syllabus.
Enders, W.: Applied Econometric Time Series, 3rd ed., Wiley, 2009
Lütkepohl, H.: New Introduction to Multiple Time Series Analysis. Springer, 2005.
Kočenda, E., Černý, A.: Elements of Time Series Econometrics: An Applied Approach, Karolinum 2007
Teaching methods -
Last update: PhDr. Jaromír Baxa, Ph.D. (07.02.2022)
Lectures will provide context and description of the empirical methods.
Students are supposed to cover selected methods in regular problem sets, that are based on replications of academic papers. Sample R codes will be provided. Problem sets are presented and discussed during the seminars.
Last update: PhDr. Jaromír Baxa, Ph.D. (07.02.2022)
Lectures will provide context and description of the empirical methods.
Students are supposed to cover selected methods in regular problem sets, that are based on replications of academic papers. Sample R codes will be provided. Problem sets are presented and discussed during the seminars.
Syllabus -
Last update: PhDr. Jaromír Baxa, Ph.D. (15.02.2023)
Lecture 1 - Intro + Review of time series models. AR, MA, ARMA models and their properties. Stationarity: economic and econometric interpretation, unit-root tests.
Lecture 2 - Nonstationary models, structural breaks, and forecasting.
Lecture 3 - Spectra, cycles, and filters. Frequency domain analysis of time series. Spectrum, periodogram.
Lecture 4 - Evaluating business cycles in real-time. Can we predict turning points?
Lecture 5 - State space and dynamic factor models: Synthesize many series for predictions.
Lecture 6 - A primer of Vector autoregressions.Identification of turning points, leading indicators, nowcasting.
Lecture 7 - Identification of VAR models. Structural VARs.
Lecture 8 - Identification of VAR models (Cont.) Sign restrictions.
Lecture 9 - Direct estimation of impulse responses: Local projections and narrative approach
Lecture 10 - VARs with nonstationary variables. Cointegration and VECM.
Lecture 11 - Bayesian VARs and Large VARs. Principles of Bayesian estimation. Bayesian VARs, FAVAR, and alternatives.
Lecture 12 - Recent approaches to identification. External instruments (proxy SVAR) and high-frequency identification. Local projections.
Lecture 13 - Nonlinear models. Univariate and multivariate nonlinear models.
Last update: PhDr. Jaromír Baxa, Ph.D. (15.02.2023)
Lecture 1 - Intro + Review of time series models. AR, MA, ARMA models and their properties. Stationarity: economic and econometric interpretation, unit-root tests.
Lecture 2 - Nonstationary models, structural breaks, and forecasting.
Lecture 3 - Spectra, cycles, and filters. Frequency domain analysis of time series. Spectrum, periodogram.
Lecture 4 - Evaluating business cycles in real-time. Can we predict turning points?
Lecture 5 - State space and dynamic factor models: Synthesize many series for predictions.
Lecture 6 - A primer of Vector autoregressions.Identification of turning points, leading indicators, nowcasting.
Lecture 7 - Identification of VAR models. Structural VARs.
Lecture 8 - Identification of VAR models (Cont.) Sign restrictions.
Lecture 9 - Direct estimation of impulse responses: Local projections and narrative approach
Lecture 10 - VARs with nonstationary variables. Cointegration and VECM.
Lecture 11 - Bayesian VARs and Large VARs. Principles of Bayesian estimation. Bayesian VARs, FAVAR, and alternatives.
Lecture 12 - Recent approaches to identification. External instruments (proxy SVAR) and high-frequency identification. Local projections.
Lecture 13 - Nonlinear models. Univariate and multivariate nonlinear models.
