Which order is too much? An application to a model with staggered price and wage contracts
AbstractFirst-order approximations to the solution to a Dynamic Stochastic General Equilibrium Model (DGSE) are now widely used in the literature. In particular, the solution is usually based on the standard log-linearisation procedure around the steady-state. However, it may be not enough especially when studying welfare across policies. In effect, welfare comparisons cannot be implemented since we need at least second-order effect on the model's deterministic steady-state. This standard method of approximation have been questioned in a series of papers. Except in rather restrictive cases (Obstfeld and Rogoff, 1998, 2002; Devereux and Engel, 2001, and Corsetti and Pesenti, 2001), it is not possible to derive explicitely an exact expression for utility-based welfare functions. Some papers have shown the importance of higher-order approxination. For example, Kim and Kim (2003a) note the importance of second-order approximation in studying welfare effects of trade in a two-country framework. A recent literature have proposed different methods to produce second-order accurate approximation to the solutions to DGSE's from a straightforward second-order approximation of the model. Among others, Judd (2002), Jin and Judd (2002) show how to compute approximation of arbitrary order on discrete-time models. Specifically, they propose a general Taylor series method for computing asymptotically valid approximations to deterministic and stochastic rational expectation models near the deterministic steady-state. Collard and Juillard (2001b), Anderson and Levin (2002), Schmitt-Grohe and Uribe (2004) apply pertubation methods of higher than first order. Sims (2002) generalised the approaches of Judd (1998), Judd and Gaspar (1997) and Judd and Guu (1993) in order to find a second-order accurate solution of discrete-time dynamic equilibrium models. Kim et al. (2003) propose an algorithm in order to compute a second-order approximation in which the error in the approximation is claimed to converge in probability to zero and does not depend on strict boundedness of the support of the distribution of the shocks. Then they apply their method for calculating forecasts and impulse-response functions in DGSE\ models. In this paper, we investigate the accuracy of k-order perturbation method in approximating the solution of a DSGE model. As a benchmark model, we use a version of Erceg, Henderson and Levin (2000) model with staggered price and wage contracts. Using different criteria, we assess to what extent the order of the approximation matters and which order is reliable. Our results show that standard first-order and second-order approximation may lead to misleading interpretations. At the same time, over-approximating the model may also conduct to substantial distortions.
Download InfoTo our knowledge, this item is not available for download. To find whether it is available, there are three options:
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
Bibliographic InfoPaper provided by Society for Economic Dynamics in its series 2004 Meeting Papers with number 635.
Date of creation: 2004
Date of revision:
Contact details of provider:
Postal: Society for Economic Dynamics Christian Zimmermann Economic Research Federal Reserve Bank of St. Louis PO Box 442 St. Louis MO 63166-0442 USA
Web page: http://www.EconomicDynamics.org/society.htm
More information through EDIRC
Approximation methods; Perturbations; Dynamic stochastic general equilibrium models;
Other versions of this item:
- Florian PELGRIN & Michel JUILLARD, 2004. "Which order is too much? An application to a model with staggered price and wage contratcs," Computing in Economics and Finance 2004 58, Society for Computational Economics.
- C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
- E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statistics
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christian Zimmermann).
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.