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Forecasting using a Nonlinear DSGE Model

Author

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  • Sergey Ivashchenko

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

  • Rangan Gupta

    () (Corresponding author, Department of Economics, University of Pretoria, Pretoria, South Africa)

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, 2018. "Forecasting using a Nonlinear DSGE Model," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 7(2), pages 73-98.
  • Handle: RePEc:cbk:journl:v:7:y:2018:i:2:p:73-98
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    Keywords

    Nonlinear DSGE; Quadratic Kalman Filter; Out-of-sample forecasts.;

    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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