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    題名: Using the Hybrid Phillips Curve with Memory to Forecast U.S. Inflation
    作者: 朱琇妍
    Chu, Shiou-Yen
    Shane, Christopher
    貢獻者: 財政系
    關鍵詞: autoregressive fractionally integrated moving average; inflation persistence; out-of-sample forecasting; quasi in-sample forecasting
    日期: 2017-07
    上傳時間: 2019-10-04 16:08:59 (UTC+8)
    摘要: This paper adopts the Caputo fractional derivative to re-specify the hybrid Phillips curve as a dynamic process of inflation with memory. The Caputo fractional derivative contains a non-integer differencing order, providing the same insight for persistence as emphasized in the Autoregressive Fractionally Integrated Moving Average (ARFIMA) time series models. We utilize the hybrid Phillips curve with memory to forecast US inflation during 1967–2014. The results indicate that our model performs well against a traditional hybrid Phillips curve, an integrated moving average model and a naive random walk model in quasi-in-sample forecasts. In out-of-sample forecasts based on Consumer Price Index (CPI) and Personal Consumption Expenditure (PCE) data, we find that the forecasting performance of Phillips curve models depends on the sample period. Our model with CPI data can outperform others in out-of-sample forecasts during and after the most recent financial crisis (2006–2014).
    關聯: Studies in Nonlinear Dynamics and Econometrics, Vol.21, No.4, pp.117-133
    資料類型: article
    DOI 連結: https://doi.org/10.1515/snde-2016-0088
    DOI: 10.1515/snde-2016-0088
    顯示於類別:[財政學系] 期刊論文

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