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.