Factor Models and Time‐Varying Parameter Framework for Forecasting Exchange Rates and Inflation: A Survey
Factor Models and Time‐Varying Parameter Framework for Forecasting Exchange Rates and Inflation: A Survey
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用于预测汇率和通货膨胀的因子模型和时变参数框架：一项调查
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Manouchehr Mokhtari
中科院分区：
文献类型：
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作者：
L. Kavtaradze;Manouchehr Mokhtari
对用于预测汇率和通货膨胀的模型的调查表明，基于因素和随时间变化的参数或状态空间模型相对于所有其他模型生成上级预测。本研究亦发现以泰勒法则及资产组合平衡理论为基础的模型对汇率的预测能力适中。使用贝叶斯模型平均法预测汇率的证据显示，预测能力有限，但对预测通货膨胀有很强的支持。总体而言，绝大多数证据表明，预测背景、历史数据的相关性、数据转换、基准选择、选定的时间范围、样本期和预测评估方法是选择汇率和通货膨胀预测模型的关键因素。
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.