Using the hybrid Phillips curve with memory to forecast US inflation

Chu Shiou-Yen () and Shane Christopher
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Chu Shiou-Yen: Department of Economics, National Chung Cheng University, 168, University Rd., Min-Hsiung, Chia-Yi 62102, Taiwan, Phone: +886-5-2720411 ext. 34168, Fax: 886-5-2720816
Shane Christopher: Institute of Economics, National Sun Yat-sen University, Kaohsiung 80424, Taiwan

Studies in Nonlinear Dynamics & Econometrics, 2017, vol. 21, issue 4, 16

Abstract: 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).

Keywords: autoregressive fractionally integrated moving average; inflation persistence; out-of-sample forecasting; quasi in-sample forecasting (search for similar items in EconPapers)
JEL-codes: C53 C63 E31 (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1515/snde-2016-0088

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