Economics
416
Fall,
2013
Advanced
Macroeconomics: Estimation and Analysis of Dynamic Macroeconomic Models
The course is the first in the three-part 416 series. The
course focuses on a mixture of methodological tools and economic substance
relevant to empirical macroeconomics. The course evaluation is based on a
midterm, a final and weekly homeworks.
The final may be replaced by a term paper. The recommended computer
software is MATLAB and Dynare.
1. Solution
and stochastic simulation of dynamic models (software
used to generate the graphs in the handout, a zip
file that uses Dynare to do some of the computations).
a. Perturbation methods and pruning.
b. Projection methods and dynamic programming.
c. Applications: real business cycle models, later:
models with sticky prices.
d. Extended discussion of first order perturbation:
Blanchard-Kahn conditions for determinacy.
e. References:
i.
Christiano-Fisher
(JEDC,
2000)
ii.
Ken Judd (Numerical Methods in Economics, MIT
Press, 1998).
iii.
Kim-Kim-Schaumburg-Sims
(JEDC, 2008) .
iv.
den Haan-de Wind (2009).
v.
Lombardo
(2011)
vi.
Andreasen,
Fernandez-Villaverde and Rubio-Ramirez (2013).
vii.
Mario Miranda and
Paul Fackler, Applied
Computational Economics and Finance, MIT Press, 2002 (codes).
2. Simple
New Keynesian model.
a. Economic foundations and properties of the model.
b. Solution and analysis using perturbation methods (Rotemberg
and Calvo
sticky price models.)
c. References for the model:
i.
Gali, Unemployment Fluctuations and Stabilization
Policies: A New Keynesian Perspective, MIT Press; Monetary Policy, Inflation and the Business Cycle: An Introduction to
the New Keynesian Framework, Princeton University;
ii.
Woodford, Interest and Prices: Foundations of a Theory
of Monetary Policy, Princeton University Press.
3. Aspects of time series analysis.
a. State space representation of a model (for lecture
notes see lecture notes associated with topic 4).
b. Kalman filtering and smoothing.
c. Components representations in time series analysis.
d. Reference: James Hamilton, Time Series Analysis.
4. Methods
for Bayesian inference.
a. Bayes’ rule.
b. Integration: Monte Carlo and Quadrature.
c. The Metropolis-Hastings algorithm for computing the
posterior distributions of parameters.
d. Laplace approximation to the posterior distribution
and Geweke’s modified harmonic mean estimator of marginal likelihood.
e. Illustration of Bayesian estimation methods using
artificial data generated from simple NK model.
f. References: Smets and Wouters (AER, 2007); An and Schorfheide (Econometric Reviews,
2007); Zellner, Introduction to Bayesian
Inference in Econometrics (1971). For a discussion of a Bayesian version of
GMM, section 3.3.3 here.
To see how model properties such as variances and impulse responses can be
incorporated into priors, see.
For a rigorous discussion of the parameter in the jump distribution, see.
5. Extensions of dynamic models
i.
The
‘timeless perspective’.
ii.
Time
inconsistency.
b. Financial frictions
i.
Hidden effort
models in banking.
ii.
Dynamic
contracts in dynamic models (related work: Albuquerque-Hopenhayn, 2004, RESTUD, vol. 71, No. 2; and Jonathan
Thomas and Tim Worrall, 1994, ‘Foreign Direct Investment and the Risk
of Expropriation,’ RESTUD, vol. 61, pp. 81-108).
c. The labor market (background).
i.
Motivation
for ‘sticky wages’, and a critique. (Macro
Annual discussion of Gali-Smets-Wouters, slides.)
ii.
Extensions
of the Diamond-Mortensen-Pissarides approach. (Manuscript.)
d. Implications
of the zero lower bound on nominal interest rates.
i.
The vulnerability
to deep depression, the impact on the government spending multiplier.
ii.
Multiplicity
of equilibria and equilibrium selection (related
material, including exercise).
iii.
References: Christiano,
Eichenbaum and Rebelo (2011), Eggertsson and Woodford
(2003).
Homework (please
email these to me in typed format)
Homework
#3 (related reading
#1 and reading
#2).
Midterm and Homework #5 (to be done by
each student independently). Code.
Homework #6
Homework #7