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Piecewise Linear Solutions for Non-Stationary Models

Author

Listed:
  • Mariano Kulish

    (Univeristy of Sydney)

  • Inna Tsener

    (Universitat de les Illes Balears)

Abstract

We assess the accuracy and efficiency of time-varying linear solution methods for non-stationary rational expectations models. These methods construct a sequence of local linear approximations, each with coefficients that vary over time, based on a set of expansion points. Benchmarking against globally accurate non-linear solutions, we show, both theoretically and numerically, that their accuracy depends critically on the choice of expansion points and on agents’ expectations about the future. Our results contribute to the literature on solving non-stationary stochastic models with rational expectations, spanning a wide range of sources of non-stationarity, including evolving structural parameters, changing policy regimes, and cases without a balanced growth path.

Suggested Citation

  • Mariano Kulish & Inna Tsener, 2025. "Piecewise Linear Solutions for Non-Stationary Models," Working Papers 387, Red Nacional de Investigadores en Economía (RedNIE).
    • Handle: RePEc:aoz:wpaper:387
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    File URL: https://rednie.eco.unc.edu.ar/files/DT/387.pdf
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    JEL classification:

    • 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
    • C68 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computable General Equilibrium Models

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