On the forecasting performance of small-scale DGSE models: a Monte Carlo evaluation and an application to UK
通过蒙特卡洛模拟和英国1986-2019年季度数据，比较新小规模DSGE模型与VAR等模型的预测表现，发现新模型在方向预测上更优，但密度预测无显著差异。
This article investigates the forecasting performance of a new small-scale dynamic stochastic general equilibrium (DSGE) model. To this end, this article first conducts a Monte Carlo study for the evaluation of the new model against another DSGE model , a standard vector autoregression (VAR), and a Bayesian VAR, using root mean square error and directional accuracy measures. An empirical application to UK quarterly data of output gap, inflation, and interest rates over the period 1986–2019 is also carried out for point and density forecasts. The empirical findings unveil a better performance of the new model for directional accuracy than for root mean square error, while density forecasts indicate no statistical differences among the new model and the VAR models.