Dear Dynare users,
Please find below the list of the first papers added to the Dynare
Working Papers series.
If you want to add your paper to the series, instructions on how to
proceed are on: http://www.dynare.org/wp/desc
Summary of contents:
#9: Getting Normalization Right: Dealing with ‘Dimensional Constants’ in Macroeconomics
Cristiano Cantore, Paul Levine
#8: Indirect Likelihood Inference
Michael Creel, Dennis Kristensen
#7: Évaluation de la politique monétaire dans un modèle DSGE pour la zone euro
Stéphane Adjemian, Antoine Devulder
#6: Taking Perturbation to the Accuracy Frontier: A Hybrid of Local and Global Solutions
Lilia Maliar, Serguei Maliar, Sébastien Villemot
#5: Switching Monetary Policy Regimes and the Nominal Term Structure
Marcelo Ferman
#4: Products, patents and productivity persistence: A DSGE model of endogenous growth
Tom Holden
#3: A Graphical Representation of an Estimated DSGE Model
Mariano Kulish, Callum Jones
#2: Solving rational expectations models at first order: what Dynare does
Sébastien Villemot
#1: Dynare: Reference Manual, Version 4
Stéphane Adjemian, Houtan Bastani, Michel Juillard, Ferhat Mihoubi, George Perendia, Marco Ratto, Sébastien Villemot
Contents:
#9: Getting Normalization Right: Dealing with ‘Dimensional Constants’ in Macroeconomics
By: Cristiano Cantore
Paul Levine
Date: 2011-07
PDF: http://www.dynare.org/wp-repo/dynarewp009.pdf
Source: http://www.dynare.org/wp-repo/dynarewp009.zip
We contribute to a recent literature on the normalization,
calibration and estimation of CES production functions. The problem arises
because CES ‘share’ parameters are not in fact shares, but depend on
underlying dimensions - they are ‘dimensional constants’ in other words. It
follows that such parameters cannot be calibrated, nor estimated unless the
choice of units is made explicit. We use an RBC model to demonstrate two
equivalent solutions. The standard one expresses the production function in
deviation form about some reference point, usually the steady state of the
model. Our alternative, ‘re-parametrization’, expresses dimensional constants
in terms of a new dimensionless (share) parameter and all remaining
dimensionless ones. We show that our ‘re-parametrization’ method is equivalent
and arguably more straightforward than the standard normalization in deviation
form. We then examine a similar problem of dimensional constants for CES
utility functions in a two-sector model and in a small open economy model;
then re-parametrization is the only solution to the problem, showing that our
approach is in fact more general.
#8: Indirect Likelihood Inference
By: Michael Creel
Dennis Kristensen
Date: 2011-07
PDF: http://www.dynare.org/wp-repo/dynarewp008.pdf
Source: http://www.dynare.org/wp-repo/dynarewp008.zip
Given a sample from a fully specified parametric model, let $Z_n$ be
a given finite-dimensional statistic - for example, an initial estimator or a
set of sample moments. We propose to (re-)estimate the parameters of the
model by maximizing the likelihood of $Z_n$. We call this the maximum indirect
likelihood (MIL) estimator. We also propose a com- putationally tractable
Bayesian version of the estimator which we refer to as a Bayesian Indirect
Likelihood (BIL) estimator. In most cases, the density of the statistic will
be of unknown form, and we develop simulated versions of the MIL and BIL
estimators. We show that the indirect likelihood estimators are consistent and
asymptotically normally distributed, with the same asymptotic variance as that
of the corresponding efficient two-step GMM estimator based on the same
statistic. However, our likelihood-based estimators, by taking into account
the full finite-sample distribution of the statistic, are higher order
efficient relative to GMM-type estimators. Furthermore, in many cases they
enjoy a bias reduction property similar to that of the indirect inference
estimator. Monte Carlo results for a number of applications including dynamic
and nonlinear panel data models, a structural auction model and two DSGE
models show that the proposed estimators indeed have attractive finite
sample properties.
#7: Évaluation de la politique monétaire dans un modèle DSGE pour la zone euro
By: Stéphane Adjemian
Antoine Devulder
Date: 2011-05
PDF: http://www.dynare.org/wp-repo/dynarewp007.pdf
Source: http://www.dynare.org/wp-repo/dynarewp007.tar.bz2
Dans cet article nous présentons de façon détaillée un modèle DSGE
canonique et montrons comment celui-ci peut être simulé puis estimé. Nous
proposons deux applications sur la base du modèle estimé. Dans la première
nous évaluons les conséquences sur le bien être social de la forme de la
politique monétaire. On montre que le bien être social est significativement
dégradé si la Banque Centrale ne prend pas en compte l’écart de
production. Dans la seconde, nous interrogeons le modèle sur la publicité que
la Banque Centrale doit faire autour de sa politique. Nous montrons que face à
un choc de productivité négatif il est préférable de ne pas annoncer une
politique monétaire accommodante, afin de limiter l’ampleur des
tensions inflationnistes.
#6: Taking Perturbation to the Accuracy Frontier: A Hybrid of Local and Global Solutions
By: Lilia Maliar
Serguei Maliar
Sébastien Villemot
Date: 2011-05
PDF: http://www.dynare.org/wp-repo/dynarewp006.pdf
Source: http://www.dynare.org/wp-repo/dynarewp006.tar.gz
Perturbation methods produce solutions of lower accuracy than global
Euler equation-based methods. In the present paper, we implement a hybrid
method that solves for some policy functions locally (using standard
perturbation) and solves for the other policy functions globally (using
closed-form expressions and a numerical solver). Our hybrid method extends the
current speed-accuracy frontier: for a multi-country RBC model used for
comparing numerical methods in a special 2011 issue of the JEDC, we attain
higher accuracy of solutions than any other method participating in the
comparison analysis. Our solutions are computed with the help of Dynare, and
the programs are publicly available.
#5: Switching Monetary Policy Regimes and the Nominal Term Structure
By: Marcelo Ferman
Date: 2011-05
PDF: http://www.dynare.org/wp-repo/dynarewp005.pdf
Source: http://www.dynare.org/wp-repo/dynarewp005.mod
This paper builds a dynamic stochastic general equilibrium (DSGE)
model of endogenous growth that is capable of generating substantial degrees
of endogenous persistence in productivity. When products go out of patent
protection, the rush of entry into their production destroys incentives for
process improvements. Consequently, old production processes are enshrined in
industries producing non-protected products, resulting in aggregate
productivity persistence. Our model also generates sizeable delayed movements
in productivity in response to preference shocks, providing a form of
endogenous news shock. Finally, if we calibrate our model to match a high
aggregate mark-up then we can replicate the negative response of hours to a
positive technology shock, even without the inclusion of any frictions.
#4: Products, patents and productivity persistence: A DSGE model of endogenous growth
By: Tom Holden
Date: 2011-05
PDF: http://www.dynare.org/wp-repo/dynarewp004.pdf
Source: http://www.dynare.org/wp-repo/dynarewp004.zip
This paper builds a dynamic stochastic general equilibrium (DSGE)
model of endogenous growth that is capable of generating substantial degrees
of endogenous persistence in productivity. When products go out of patent
protection, the rush of entry into their production destroys incentives for
process improvements. Consequently, old production processes are enshrined in
industries producing non-protected products, resulting in aggregate
productivity persistence. Our model also generates sizeable delayed movements
in productivity in response to preference shocks, providing a form of
endogenous news shock. Finally, if we calibrate our model to match a high
aggregate mark-up then we can replicate the negative response of hours to a
positive technology shock, even without the inclusion of any frictions.
#3: A Graphical Representation of an Estimated DSGE Model
By: Mariano Kulish
Callum Jones
Date: 2011-05
PDF: http://www.dynare.org/wp-repo/dynarewp003.pdf
Source: http://www.dynare.org/wp-repo/dynarewp003.zip
We write a New Keynesian model as an aggregate demand curve and an
aggregate supply curve, relating inflation to output growth. The graphical
representation shows how structural shocks move aggregate demand and supply
simultaneously. We estimate the curves on US data from 1948 to 2010. The Great
Recession in 2008-09 is explained by a collapse of aggregate demand driven by
adverse preference and permanent technology shocks, and expectations of
low inflation.
#2: Solving rational expectations models at first order: what Dynare does
By: Sébastien Villemot
Date: 2011-04
PDF: http://www.dynare.org/wp-repo/dynarewp002.pdf
This paper describes in detail the algorithm implemented in Dynare
for computing the first order approximated solution of a nonlinear rational
expectations model. The core of the algorithm is a generalized Schur
decomposition (also known as the QZ decomposition), as advocated by several
authors in the litterature. The contribution of the present paper is to focus
on implementation details that make the algorithm more generic and more
efficient, especially for large models.
#1: Dynare: Reference Manual, Version 4
By: Stéphane Adjemian
Houtan Bastani
Michel Juillard
Ferhat Mihoubi
George Perendia
Marco Ratto
Sébastien Villemot
Date: 2011-04
PDF: http://www.dynare.org/wp-repo/dynarewp001.pdf
Dynare is a software platform for handling a wide class of economic
models, in particular dynamic stochastic general equilibrium (DSGE) and
overlapping generations (OLG) models. The models solved by Dynare include
those relying on the rational expectations hypothesis, wherein agents form
their expectations about the future in a way consistent with the model. But
Dynare is also able to handle models where expectations are
formed differently: on one extreme, models where agents perfectly anticipate
the future; on the other extreme, models where agents have limited rationality
or imperfect knowledge of the state of the economy and, hence, form their
expectations through a learning process. Dynare offers a user-friendly and
intuitive way of describing these models. It is able to perform simulations of
the model given a calibration of the model parameters and is also able to
estimate these parameters given a dataset. Dynare is a free software, which
means that it can be downloaded free of charge, that its source code is freely
available, and that it can be used for both non-profit and
for-profit purposes.
--
Sébastien Villemot
Researcher in Economics at CEPREMAP & Debian Maintainer
http://www.dynare.org/sebastien
Phone: +33-1-40-77-49-90 - GPG Key: 4096R/381A7594
