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Tools for Modern Macroeconometrics

Fakulta sociálních věd UK
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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.



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.



Kilian, L., & Lütkepohl, H.: Structural Vector Autoregressive Analysis. Cambridge: Cambridge University Press, 2017.

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

07.03.2022 - 14:24