Efficient representation of state spaces for some dynamic models

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Many important economic problems require computation over state spaces that are not hypercubes. Examples include industry models of multi-product differentiated product firms, Bayesian learning problems with noisy signals and real business cycle models with heterogeneous agents. These problems have not been analyzed partly because of the difficulty in efficiently representing their state spaces on a computer. I develop a representation algorithm for the state spaces of the above problems, which potentially allows them to be solved with computational methods such as dynamic programming. I find that using this representation reduces the computation time and space by several orders of magnitude relative to a naïve representation.

Original languageEnglish (US)
Pages (from-to)1077-1098
Number of pages22
JournalJournal of Economic Dynamics and Control
Volume23
Issue number8
StatePublished - Aug 1999
Externally publishedYes

Fingerprint

Dynamic models
Dynamic Model
State Space
Computational methods
Dynamic programming
Industry
Real Business Cycles
Bayesian Learning
Heterogeneous Agents
Economics
Hypercube
Computational Methods
Dynamic Programming
State space
Model
Bayesian learning
Differentiated products
Real business cycle models
Heterogeneous agents

Keywords

  • C63
  • Computation
  • Dynamic models
  • State spaces

ASJC Scopus subject areas

  • Economics and Econometrics
  • Control and Optimization

Cite this

Efficient representation of state spaces for some dynamic models. / Gowrisankaran, Gautam.

In: Journal of Economic Dynamics and Control, Vol. 23, No. 8, 08.1999, p. 1077-1098.

Research output: Contribution to journalArticle

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