Predicting Long‐Term Financial Returns: VAR versus DSGE Model—A Horse Race
比较了无约束向量自回归（VAR）和完全结构化的动态随机一般均衡（DSGE）模型在预测长达15年的金融回报方面的表现，发现DSGE模型在长期预测中更优，并能产生更高的夏普比率。
Abstract This paper considers a U.S. institutional investor who is implementing a long‐term portfolio allocation using forecasts of financial returns. We compare the predictive performance of two competing macrofinance models—an unrestricted vector autoRegression (VAR) and a fully‐structural dynamic stochastic general equilibrium (DSGE) model—for horizons up to 15 years. Although the performances are similar for short horizons, the DSGE model outperforms the VAR at forecasting financial returns in the long term. This model also generates substantially higher Sharpe ratios. Although it contains fewer unknown parameters, it benefits from economically grounded restrictions that help anchor financial returns in the long term.