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A Reliable Technique for Accurately Computing Unconditional Variances

Author

Listed:
  • Gary S. Anderson

    (Board of Governors, Federal Reserve System)

Abstract

This paper provides formulae for computing perturbation method approximations of unconditional variances of variables in nonlinear DSGE models. Spurious higher order terms that creep into multi-step ahead forecasts can produce explosive time paths frustrating traditional approaches to estimating unconditional covariances. They have developed a pruning solution to preempt this specious explosive behavior. This paper outlines a more direct approach to approximating unconditional covariances. By, in effect, explicitly including long forecast of powers of endogenous variables among the DSGE model equations, one can obtain perturbation method approximations for the covariances along with the other Taylor series approximation equations. Explicit formulae for computing perturbation solutions for models with multiple leads makes including such long horizon forecasts computational feasible. Furthermore, in this formulation, the coefficients associated with the initial conditions for the state variables provide useful diagnostic information about the accuracy of the unconditional variance approximation. This paper (i) applies the technique to linear models, where explicit formulae for unconditional covariances are available, to motivate and validate the performance of the technique. (ii) contrasts and compares the accuracy, computational, efficiency and tractability for this method and the pruning method.

Suggested Citation

  • Gary S. Anderson, 2006. "A Reliable Technique for Accurately Computing Unconditional Variances," Computing in Economics and Finance 2006 291, Society for Computational Economics.
  • Handle: RePEc:sce:scecfa:291
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    References listed on IDEAS

    as
    1. Badi Baltagi & Dong Li, 2006. "Prediction in the Panel Data Model with Spatial Correlation: the Case of Liquor," Spatial Economic Analysis, Taylor & Francis Journals, vol. 1(2), pages 175-185.
    2. Elhorst, J. Paul, 2001. "Panel data models extended to spatial error autocorrelation or a spatially lagged dependent variable," Research Report 01C05, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    3. Giuseppe Arbia & Gianfranco Piras, 2004. "Convergence in per-capita GDP across European regions using panel data models extended to spatial autocorrelation effects," ERSA conference papers ersa04p524, European Regional Science Association.
    4. repec:dgr:rugsom:01c05 is not listed on IDEAS
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    More about this item

    Keywords

    perturbation method; DSGE; unconditional covariance;

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools

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