使用循环神经网络预测通货膨胀
Predicting Inflation with Recurrent Neural Networks
AI总结：
本文采用LSTM循环神经网络预测美国通货膨胀，并分析其隐含经济因子，发现其长期及高不确定性时期表现具竞争力，且因子与商业周期高度相关。
摘要：
本文将一种循环神经网络——LSTM——应用于通货膨胀预测。该模型对时间序列颇具吸引力，因为它按顺序处理每个时间步，并显式学习动态依赖关系。本文还探究了该模型的降维能力，以揭示能够解释通货膨胀过程的、具有经济意义的因子。基于美国数据的实验结果表明，与常用基准（包括其他机器学习模型）相比，所估计的神经网络表现具有竞争力，但并不突出。特别地，研究发现LSTM在长期预测区间以及宏观经济不确定性加剧的时期表现良好。有趣的是，LSTM隐含因子与商业周期指标呈现高度相关性，这说明此类信号作为通货膨胀预测因子的有用性。本文还阐明了网络初始化和架构对预测性能的影响。
英文摘要：
This paper applies a recurrent neural network, the LSTM, to forecast inflation. This is an appealing model for time series as it processes each time step sequentially and explicitly learns dynamic dependencies. The paper also explores the dimension reduction capability of the model to uncover economically-meaningful factors that can explain the inflation process. Results from an exercise with US data indicate that the estimated neural nets present competitive, but not outstanding, performance against common benchmarks (including other machine learning models). The LSTM in particular is found to perform well at long horizons and during periods of heightened macroeconomic uncertainty. Interestingly, LSTM-implied factors present high correlation with business cycle indicators, informing on the usefulness of such signals as inflation predictors. The paper also sheds light on the impact of network initialization and architecture on forecast performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
