Econometrics III · Economics
I completed the first year phd econometrics .
An overview of Bayesian and frequentist methods used for identification and estimation in macroeconomic models.
We learned a huge amount of topics related to macroeconomic methods in econometrics.
(i) Setting up numerical optimization-type problems, e.g. covariance structure estimation, indirect least squares, GMM estimation. (ii) Kalman Filtering (iii) Bayesian computation techniques, e.g. Gibbs sampling, Metropolis-Hastings. (iv) Time series concepts, including diagnostics, VAR, SVAR.
Time series, in particular ARMA models (going beyond the material Serena teaches to first year PhDs); Bayesian estimation; State space models, Kalman filters, and recursive estimation; VAR and SVAR (which may have been touched upon in first year macro).
I think this is a must take if you're doing macro, but might not be very useful for non-macro students. Although it does cover some material applicable outside of macro (ie Bayesian methods) and GMM type estimators. 1 Columbia University: Arts & Sciences Fall 2022 Course: ECONGR6413_001_2022_3-MACRO-ECONOMETRICS : ECONGR6413_001_2022_3 - MACRO-ECONOMETRICS Instructor: Serena Ng