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OLG Life Cycle Model Transition Paths: Alternate Model Forecast Method

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  • Richard W. Evans

    ()
    (Department of Economics, Brigham Young University)

  • Kerk L. Phillips

    ()
    (Department of Economics, Brigham Young University)

Abstract

The overlapping generations (OLG) model is an important framework for analyzing any type of question in which age cohorts are affected differently by exogenous shocks. However, as the dimensions and degree of heterogeneity in these models increase, the computational burden imposed by rational expectations solution methods for nonstationary equilibrium transition paths increases exponentially. As a result, these models have been limited in the scope of their use to a restricted set of applications and a relatively small group of researchers. In addition to providing a detailed description of the benchmark rational expectations computational method, this paper presents an alternative method for solving for equilibrium transition paths in OLG life cycle models that is new to this class of model. The key insight is that even naive limited information forecasts within the model produce aggregate time series similar to full information rational expectations time series as long as the naive forecasts are updated each period. We find that our alternate model forecast method reduces computation time by 85 percent, and the approximation error is small.

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File URL: http://economics.byu.edu/Documents/Macro%20Lab/Working%20Paper%20Series/BYUMCL2012-04.pdf
File Function: First version, 2012
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Bibliographic Info

Paper provided by Brigham Young University, Department of Economics, BYU Macroeconomics and Computational Laboratory in its series BYU Macroeconomics and Computational Laboratory Working Paper Series with number 2012-04.

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Length: 35 pages
Date of creation: Apr 2012
Date of revision:
Publication status: Published in Computational Economics, January 2014, Volume 43, Issue 1, pp 105-131
Handle: RePEc:byu:byumcl:201204

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Keywords: Computable General Equilibrium Models; Heterogeneous Agents; Overlapping Generations Model; Distribution of Savings;

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