PhD Course: Topics in advanced
macroeconometrics I
4-day mini course
Lecturer:
Hilde C. Bjørnland,
Location:
The course
is credited with 5 ECTS in the PhD - program in economics at the
Monday 11
December, 13:15–16
Tuesday 12
December, 9:15-12
Wednesday
13 December, 9:15–11
Thursday 14
December, 9:15–12, 13:15-16
Following
the pioneering work by Sims, macroeconomists have become increasingly
preoccupied with the analysis of sources of economic fluctuations. Structural
vector autoregressive (VAR) models have made it possible to analyze the
importance of various structural shocks, and are among the main tools used in applied macroeconomic
modeling today. Structural VARs have also obtained an important position as
tool for policy evaluation.
This course will provide a thorough
assessment of structural VARs. The course will be
divided into three parts. The first part will discuss the fundamentals of
structural VARs, including the Wold theorem,
specification issues and the use of impulse responses and variance
decompositions as a way to summarize the information content of VARs. Some preliminaries will also be covered, including a
brief introduction to advanced time series concepts, stochastic processes and
basic asymptotic theory.
The second part deals with the issue of
transforming the information content of reduced form dynamics into structural
relationships. Identification using short run and long run restrictions on the
covariance matrix will be discussed. Recent policy experiments using a variety
of restrictions will be used as examples.
The third and final part covers
sign restrictions and Bayesian vectorautoregression.
Bayesian VAR was originally developed as a way to improve out of sample
forecast, but are now used for a variety of purposes,
including policy analysis. Sign restrictions provide an alternative way of
identifying structural shocks when we have no a priori reasoning for using zero
short (or long) run restrictions. More recently, sing restrictions have been
used to bridge the gap between DSGE models and VARs.
·
Time series analysis
·
Identification
problem in econometrics
a) Time series analysis
·
Preliminaries
·
Covariance
stationary vector process
·
Vector
moving average (MA) representation
·
The
Wold theorem
b) VAR – specification, estimation and information content
·
Impulse response function
·
Forecast
error variance decompositions
·
Historical
decompositions
c) Identification of structural VARs
·
Structural
vs. behavioral models
·
Cholesky decomposition
·
SVAR
and contemporaneous restrictions
·
Long-run
restrictions
d) Sign restrictions and Bayesian VAR
·
Bayesian
inference
·
Priors
for VARs
·
Sign
restrictions
a) Potential, limitations and controversies (puzzles) in the SVAR
literature.
·
Controversies
using long run restrictions
·
Monetary
policy analysis – puzzles
·
Conclusions
Blanchard Olivier
J. and Danny Quah (1989), “The dynamic effects of
aggregate demand and supply disturbances”, American Economic Review, 79,
655-673.
Christiano, L.J., Eichenbaum M. and C.L. Evans, (1999) “Monetary Policy
Shocks: What Have We Learned and to What End?” in John Taylor and Michael
Woodford, eds. Handbook of Macroeconomics Elsevier Science Ltd.
Favero, Carlo A. (2001) “Applied Macroeconometrics”,
Hamilton, James D. (1994) “Time Series Analysis”,
Lütkepohl, Helmut (1993) “Introduction to Multiple Time Series
Analysis”, Spring Verlag.
Sims, Christopher
A. (1980), “Macroeconomics and reality”, Econometrica,
48, 1-48.
Additional literature
Bernanke,
B. and 1. Mihov (1998) “Measuring
Monetary Policy,” Quarterly Journal of Economics, 113, 869-902.
Bjørnland, H.C. (2005), “Monetary Policy and Exchange Rate Interactions in a Small Open Economy,” Working Paper 2005/16, Norges Bank.
Bjørnland, H.C. (2006), “Monetary policy and exchange rate
overshooting: Dornbusch was right after all”,
Manuscript,
Bjørnland, H.C. and K. Leitemo
(2005), “Identifying the Interdependence between US Monetary Policy and the
Stock Market,” Manuscript,
Canova F. and G.
De Nicoló (2002), “Monetary disturbances matter for
business fluctuations in the G-7”, Journal of Monetary Economics, 49,
1131-1159.
Chari, V.V., Patrick
Kehoe and Ellen McGrattan, 2005, “A Critique of
Structural VARs Using Real Business Cycle Theory,” Federal Reserve Bank of
Christiano, L., Eichenbaum, M. and C.L. Evans (2005), “Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy,” Journal of Political Economy 113, 1-45.
Christiano, Lawrence J, Eichenbaum, Martin and Robert Vigfusson, (2006), “Alternative Procedures for Estimating Vector Autoregressions Identified with Long-Run Restrictions”, Journal of the European Economic Association, 4, 475-483.
Christiano, Lawrence J, Eichenbaum, Martin and Robert Vigfusson, (2006), “Assessing Structural VARs,” National Bureau of Economic Research Working Paper no. 12353.
Marco Del Negro and Frank Schorfheide, (2004). “Priors from General Equilibrium
Models for VARS,” International Economic Review, Department of Economics,
University of Pennsylvania and Osaka University Institute of Social and
Economic Research Association, 45, 643-673.
Doan,
Thomas, Robert Litterman, and Christopher Sims
(1984): Forecasting and Conditional Projections Using Realistic Prior
Distributions," Econometric Reviews, 3, 1-100.
Eichenbaum, M. and C. Evans (1995), “Some empirical evidence on the effects of
shocks to monetary policy on exchange rates,” Quarterly Journal of Economics, 110, 975-1010.
Faust, Jon (1998), “The Robustness Of Identified VAR Conclusions About Money,” Carnegie-Rochester Conference Series on
Public Policy, 49, 207-244.
Faust, Jon and Eric M. Leeper (1997) “When Do Long-Run Identifying Restrictions
Give Reliable Results?” Journal of Business and Economic
Statistics. 345 ? 353.
Faust, Jon and J.H. Rogers (2003), “Monetary policy's role in exchange rate behaviour”, Journal of Monetary Economics, 50, 1403-1424.
Fernandez-Villaverde, J., J.Rubio-Ramirez, and T. Sargent, (2005), “A, B, C’s (and D’s) for Understanding VAR’s,” National Bureau of Economic Research Technical Working Paper no. 308 and forthcoming, American Economic Review.
Gali, J.,(1992)
“How Well Does the ISLM Model Fit Postwar
Gali J. (1999), “Technology, employment,
and the business cycle: do technology shocks explain aggregate fluctuations?”, American Economic Review, Vol
89, p 249-271.
Gali,
Jordi and
Kim, S. and N. Roubini (2000), “Exchange rate anomalies in the industrial countries: A solution with a structural VAR approach”, Journal of Monetary Economics, 45, 561-586.
Leeper, E., C. Sims, and T. Zha, (1996), “What Does Monetary Policy Do?,” Brookings Papers on Economic Activity, 2, 1-78.
Rudebusch, G. (1998) “Do Measures
of Monetary Policy in a VAR Make Sense?” International
Economic Review 39, 943-48.
Sims, Christopher A. and Tao Zha (1998) “Bayesian Methods For
Dynamic Multivariate Models,” International
Economic Review, 39, 949-968.
Sims, Christopher A. and Tao Zha (1999) “Error Bands For Impulse Responses,” Econometrica, 67,
1113-1155.
Stock,
James J. and Mark W. Watson (2001): Vector Autoregressions,"
Journal of Economic Perspectives, 15, 101-115.
Uhlig Harald.
(2005), “What are the effects of Monetary Policy: Results from an Agnostic
Identification Approach”, Journal of Monetary Economics, 52, 381-419.
Uhlig, Harald
(1998) “The Robustness Of Identified VAR Conclusions
About Money. A comment”, Carnegie-Rochester
Conference Series on Public Policy, 49, 245-263.