Model Uncertainty and Exchange Rate Forecasting
研究了在信息不确定和不完整情况下，投资者如何关注一组随时间变化的小规模基本面因素，并设计了一个模型选择规则来预测汇率。该规则在样本外测试中显著优于随机游走模型，并产生了有意义的投资利润。
Exchange rate models with uncertain and incomplete information predict that investors focus on a small set of fundamentals that changes frequently over time. We design a model selection rule that captures the current set of fundamentals that best predicts the exchange rate. Out-of-sample tests show that the forecasts made by this rule significantly beat a random walk for 5 out of 10 currencies. Furthermore, the currency forecasts generate meaningful investment profits. We demonstrate that the strong performance of the model selection rule is driven by time-varying weights attached to a small set of fundamentals, in line with theory.