<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

Forecasting using a Nonlinear DSGE Model

Saint Petersburg Institute for Economics and Mathematics (Russian Academy of Sciences); Faculty of Economics

Saint-Petersburg State University; National Research University Higher School of Economics

Journal of Central Banking Theory and Practice

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Forecasting using a Nonlinear DSGE Model"}

A medium-scale nonlinear dynamic stochastic general equilibrium (DSGE) model was estimated (54 variables, 29 state variables, 7 observed variables). The model...

Forecasting using a Nonlinear DSGE Model

Published Online: May 14, 2018

Page range: 73 - 98

Received: Jul 04, 2017

Accepted: Oct 06, 2017

DOI: https://doi.org/10.2478/jcbtp-2018-0013

Keywords
Nonlinear DSGE, Quadratic Kalman Filter, Out-of-sample forecasts

© 2017 Sergey Ivashchenko et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Forecasting using a Nonlinear DSGE Model

Published Online: May 14, 2018

Page range: 73 - 98

Received: Jul 04, 2017

Accepted: Oct 06, 2017

DOI: https://doi.org/10.2478/jcbtp-2018-0013

KeywordsNonlinear DSGE, Quadratic Kalman Filter, Out-of-sample forecasts

© 2017 Sergey Ivashchenko et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Keywords
Nonlinear DSGE, Quadratic Kalman Filter, Out-of-sample forecasts