Empirical Methods for Dynamic Macroconomics

Fall 2014

                       

            The course meets on Wednesdays from 4-6 pm in 802 19 W. 4th St. Office hours are by appointment.

           


Assignments and Grades

 

The main assignment is to write a term paper that replicates a recent publication or working paper in empirical macroeconomics.  Before the end of week 3, you should submit the title for my approval.  If you want to discuss a paper’s suitability, talk to me before the deadline. Conference-style presentations will be held at the end of the semester.  Your term paper and presentation will count for two-thirds of your grade.  A number of problem sets will also be assigned and will count for the remaining one-third. 

 


Readings

 

Links to articles are posted below.  I also recommend the following books,

 

 “Methods for Applied Macroeconomic Research” by Fabio Canova (Princeton University Press)

“Bayesian Data Analysis” by A. Gelman, J.B. Carlin, H.S. Stern, and D.B. Rubin (Chapman and Hall)

“Contemporary Bayesian Econometrics and Statistics” by John Geweke (Wiley Interscience)

 “Markov Chain Monte Carlo in Practice” by W.R. Gilks, S. Richardson, and D.J. Spiegelhalter (Chapman and Hall)

 


Introduction to Bayesian Econometrics

 

Canova, Ch. 9

Geweke, chs. 1-3

Gelman, et. al., chs. 1-4, Appendix B 

 


Bayesian Computational Methods

Gelman, et al., chs. 9-11

Geweke, ch. 4

Gilks, et. al., chs. 1, 3-5, 8-10


Bayesian VARs

Canova, ch. 10


Stochastic Volatility Models

·        Jacquier, et al., Bayesian Analysis of Stochastic Volatility Models

·        Kim, Shephard, and Chib, Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models

·        Stock and Watson: Why Has US Inflation Become Harder to Forecast?

·        Shephard, Martingale Unobserved Components Models

·        Cogley and Sargent, Measuring Price-Level Uncertainty and Instability in the US, 1850-2012

 


Bayesian Estimation of DSGE Models

Canova, ch. 11

 


Higher-Order Approximations for DSGE Models

 


Model Averaging

 

·        Sims, Probability Models for Monetary Policy Decisions

·        Cogley and Sargent, The Conquest of US Inflation: Learning and Robustness to Model Uncertainty

·        Cogley, De Paoli, Matthes, Nikolov, and ­Yates, A Bayesian Approach to Optimal Monetary Policy with Parameter and Model Uncertainty

·        Kriwoluzky and Stoltenberg, Nested Models and Model Uncertainty

·        Amisano and Geweke, Prediction Using Several Macroeconomic Models

·        Del Negro, Hasegawa, and Schorfheide, Dynamic Prediction Pools: An Investigation of Financial Frictions and Forecasting Performance

 

 


Lecture Notes

 

Introduction to Bayesian Analysis

A Point of Departure: MLE and Maximum Posterior Estimation

Higher-Order Expansions, Numerical Integration, and Non-Iterative Monte Carlo Methods Markov Chain Monte Carlo Algorithms

Markov Chain Monte Carlo Algorithms

Applications of MCMC  

Nonlinear and/or Nongaussian State-Space Models

Estimating DSGE Models with Uncertainty Shocks

Model Averaging

 

 


Problem sets

 

Problem set 1: Constructing a normal approximation to the posterior of a DSGE model Due September 17

Problem set 2: Simulating the posterior of a DSGE model by a Metropolis-Hastings algorithm Due October 15

Problem set 3: Simulating the posterior for a Linear-Gaussian State-Space model via a Gibbs Sampler Due October 29

Problem set 4: Evaluating a likelihood function via a particle filter Due November 26