IDEAS home Printed from
MyIDEAS: Log in (now much improved!) to save this paper

A decomposition method to deflate the curse of dimensionality for dynamic stochastic models

Listed author(s):
  • Mercedes Esteban-Bravo
  • F. Javier Nogales

Computing equilibria in dynamic economies is still quite challenging even though the noticeable increase in computing power, storage capacity and new approaches in the literature on computational economics. The solvability of many economic models suffers from the curse of dimensionality, which limits the planning horizon practitioners can afford for mapping a real problem into a numerically solvable dynamic model. Consequently, many standard algorithms are computationally burdensome. We propose a decomposition procedure to deflate the dimensionality problem by splitting it into manageable pieces and coordinating their solution. There are two main computational advantages in the use of decomposition methods. First, the subproblems are, by definition, smaller than the original problem and therefore much faster to solve. Second, the subproblems could have special properties such as convexity and sparsity that enable the use of efficient algorithms to solve them. Previous decomposition algorithms break into three groups: Danzting-Wolfe decomposition, Benders decomposition and augmented Lagrangian relaxation procedure. Both Danzting-Wolfe decomposition (see Danzting and Wolfe 1960) and Benders decomposition (see Benders 1962 and Geoffrion 1972) are efficient schemes to deal with convex programming problems. Extension to nonconvex problems is the augmented Lagrangian relaxation (see Rockafellar and Wets 1991), based on an estimate of the Lagrange multipliers to decompose the problem into a set of subproblems, which solutions are used to update the current estimate of the Lagrange multipliers. Applications of decomposition methods to general equilibrium computation are originally due to Mansur and Whalley (1982). They apply Danzting and Wolfe's decomposition procedure to Scarf's (1982) algorithm to improve the speed of convergence. In contrast, the current paper considers the extension of Lagrangian decomposition methods to compute dynamic economic models in their original form. Our results show the computational gain achieved through its way to break the problem into smaller subproblems and its robustness against misspecifications.

To 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.

Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2005 with number 381.

in new window

Date of creation: 11 Nov 2005
Handle: RePEc:sce:scecf5:381
Contact details of provider: Web page:

More information through EDIRC

No references listed on IDEAS
You can help add them by filling out this form.

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:sce:scecf5:381. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum)

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.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.