Poslední úprava: 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.
Poslední úprava: 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.
Podmínky zakončení předmětu -
Poslední úprava: PhDr. Jaromír Baxa, Ph.D. (07.02.2022)
Problem sets and presentations 60%, Midterm 20%, Final exam 20%. Problem sets: 10 points for each problem set at maximum. Late submission/returned PS -1 points. Presentation: 10 points (2-3 presentations per semester).
About 10 problem sets shall be expected. It is necessary to have at least 50% of points of each problem set to pass the course.
Midterm: written exam.
Final exam: presentation of the selected problem set and written exam.
Poslední úprava: PhDr. Jaromír Baxa, Ph.D. (07.02.2022)
Problem sets and presentations 60%, Midterm 20%, Final exam 20%. Problem sets: 10 points for each problem set at maximum. Late submission/returned PS -1 points. Presentation: 10 points (2-3 presentations per semester).
About 10 problem sets shall be expected. It is necessary to have at least 50% of points of each problem set to pass the course.
Midterm: written exam.
Final exam: presentation of the selected problem set and written exam.
Poslední úprava: 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
Poslední úprava: 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
Metody výuky -
Poslední úprava: 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.
Poslední úprava: 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.
Sylabus -
Poslední úprava: PhDr. Jaromír Baxa, Ph.D. (07.02.2022)
Part I. Time series analysis
Lecture 1 - Stationary linear models. AR, MA, ARMA models and their properties. Stationarity: economic and econometric interpretation, unit-root tests.
Lecture 2 - Nonstationary models, structural breaks, and seasonality.
Lecture 3 - Spectral analysis. Frequency domain analysis of time series. Spectrum, periodogram.
Lecture 4 - Filters. Popular filters and their properties. End-sample bias and data revisions.
Part II. Macroeconometric methods
Lecture 5 - State-space models. Kalman filter, state-space forms of time series models, time-varying parameters, factor models, stochastic volatility.
Lecture 6 - Turning points and nowcasting. Identification of turning points, leading indicators, nowcasting.
Lecture 7 - VAR models. Estimation, post-estimation diagnostics, and forecasting.
Lecture 8 - Identification of VAR models. Structural VAR, sign restrictions, narrative approach.
Lecture 9 - VARs with nonstationary variables. Cointegration and VECM.
Lecture 10 - Bayesian VARs and Large VARs. Principles of Bayesian estimation. Bayesian VARs, FAVAR, and alternatives.
Lecture 11 - Recent approaches to identification. External instruments (proxy SVAR) and high-frequency identification. Local projections.
Lecture 12 - Nonlinear models. Univariate and multivariate nonlinear models.
Poslední úprava: PhDr. Petr Bednařík, Ph.D. (06.06.2020)
Lecture 1 - Course overview / Introduction to Bayesian Econometrics
Lecture 2 - Normal linear regression with natural conjugate prior
Lecture 3 - Normal linear regression with other priors / Gibbs sampling
Lecture 4 - Nonlinear regression model / Metropolis Hastings algorithm
Lecture 5 - Bayesian model averaging
Lecture 6 - Bayesian vector autoregressions
Lecture 7 - Introduction to state space modelling & Kalman filter
Lecture 8 - Estimation of state-space models (classical)
Lecture 9 - Estimation of state-space models (Bayesian)
