Econometric Tools for Macroeconomics

By Lawrence J. Christiano

Overview

This course will review the key econometric tools for macroeconomics. In practice, this means the concepts of Bayesian inference combined with time series analysis.

Lectures

1) Identification and Vector Autoregressions.

a) Exercise, code. Answers by Sebastian Kohls.

b) Readings: CEE handbook chapter, ACEL.

c) Information on material not covered in lectures: for high frequency identification, see Gorodnichenko and Weber; Gertler and Karadi; Nakamura and Steinsson, For sign restrictions see Uhlig; Arias, Rubio-Ramirez and Waggoner.

2) Bayesian inference.

a) Additional material on mixed frequency observations and link between DSGE models and VARs, reading. Code for GDP example in handout.

b) Exercise. Code for estimation part of exercise. Code for MCMC part of exercise.

c) Readings: Del Negro and Schorfheide, Zellner’s classic 1971 text, Bernanke and Boivin on ‘Data Rich’ estimation.

d) Methods to do a Bayesian version of Generalized Method of Moments and to construct more plausible priors for DSGE models.

3) Bayesian Vector Autoregressions (BVAR).

a) Exercise. Data set for exercise. Code for answer. What is an inverse Wishart distribution? Minnesota priors and dummies.

b) Readings: Hurwicz, Litterman, Del Negro and Schorfheide, Robertson and Tallman, Giannone, Lenza and Primiceri

4) Kalman Filtering and Smoothing. Preliminary results.

a) Exercise.

b) Readings: James Hamilton, Time Series Analysis, Princeton University Press, 1994.

5) Dynamic Factor Models (DFM) and Factor Augmented VARs (FAVAR).

a) What is Gibbs sampling? An example.

b) Readings: Stock and Watson, Banbura, Giannone, and Reichlin, Canova and Ciccarelli.