FACTOR MODELS AND TIME‐VARYING PARAMETER FRAMEWORK FOR FORECASTING EXCHANGE RATES AND INFLATION: A SURVEY
综述了汇率和通胀预测模型，发现基于因子和时变参数或状态空间模型优于其他模型，泰勒规则和资产组合平衡模型有中等预测力，贝叶斯模型平均对通胀预测有效但对汇率预测有限。
Abstract A survey of models used for forecasting exchange rates and inflation reveals that the factor‐based and time‐varying parameter or state space models generate superior forecasts relative to all other models. This survey also finds that models based on Taylor rule and portfolio balance theory have moderate predictive power for forecasting exchange rates. The evidence on the use of Bayesian Model Averaging approach in forecasting exchange rates reveals limited predictive power, but strong support for forecasting inflation. Overall, the evidence overwhelmingly points to the context of the forecasts, relevance of the historical data, data transformation, choice of the benchmark, selected time horizons, sample period and forecast evaluation methods as the crucial elements in selecting forecasting models for exchange rate and inflation.