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.