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Forecasting using a Nonlinear DSGE Model

Author

Listed:
  • Sergey Ivashchenko

    (Saint Petersburg Institute for Economics and Mathematics (Russian Academy of Sciences), National Research University Higher School of Economics,Russia)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Pretoria)

Abstract

A medium-scale nonlinear dynamic stochastic general equilibrium (DSGE) model was estimated (54 variables, 29 state variables, 7 observed variables). The model includes an observed variable for stock market returns. The root-mean square error (RMSE) of the in-sample and out-of-sample forecasts was calculated. The nonlinear DSGE model with measurement errors outperforms AR (1), VAR (1) and the linearised DSGE in terms of the quality of the out-of-sample forecasts. The nonlinear DSGE model without measurement errors is of a quality equal to that of the linearised DSGE model

Suggested Citation

  • Sergey Ivashchenko & Rangan Gupta, 2016. "Forecasting using a Nonlinear DSGE Model," Working Papers 201659, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:201659
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    Cited by:

    1. Kuo‐Hsuan Chin, 2022. "Forecast evaluation of DSGE models: Linear and nonlinear likelihood," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1099-1130, September.

    More about this item

    Keywords

    nonlinear DSGE; Quadratic Kalman Filter; out-of-sample;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications

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