| dc.contributor | 財政系 | |
| dc.creator (作者) | 朱琇妍 | |
| dc.creator (作者) | Chu, Shiou-Yen | |
| dc.creator (作者) | Shane, Christopher | |
| dc.date (日期) | 2017-07 | |
| dc.date.accessioned | 4-Oct-2019 16:08:59 (UTC+8) | - |
| dc.date.available | 4-Oct-2019 16:08:59 (UTC+8) | - |
| dc.date.issued (上傳時間) | 4-Oct-2019 16:08:59 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/126720 | - |
| dc.description.abstract (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). | |
| dc.format.extent | 205809 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.relation (關聯) | Studies in Nonlinear Dynamics and Econometrics, Vol.21, No.4, pp.117-133 | |
| dc.subject (關鍵詞) | autoregressive fractionally integrated moving average ; inflation persistence; out-of-sample forecasting ; quasi in-sample forecasting | |
| dc.title (題名) | Using the Hybrid Phillips Curve with Memory to Forecast U.S. Inflation | |
| dc.type (資料類型) | article | |
| dc.identifier.doi (DOI) | 10.1515/snde-2016-0088 | |
| dc.doi.uri (DOI) | https://doi.org/10.1515/snde-2016-0088 | |
