Ivashchenko, SergeyGupta, Rangan2019-05-282019-05-282018Ivashchenko, S. & Gupta, R. 2018, 'Forecasting using a nonlinear DSGE model', Journal of Central Banking Theory and Practice, vol. 7, no. 2, pp. 73-98.2336-9205 (online)10.2478/jcbtp-2018-0013http://hdl.handle.net/2263/69223A 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.en© 2017 Sergey Ivashchenko et al., published by De Gruyter Open.Nonlinear DSGEQuadratic Kalman filterOut-of-sample forecastsDynamic stochastic general equilibrium (DSGE)Root-mean square error (RMSE)Article