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• W2313829901 abstract "The objective of this article is to predict, both in sample and out of sample, the consumer price index (CPI) of the US economy based on monthly data covering the period of 1980:1–2013:12, using a variety of linear (random walk (RW), autoregressive (AR) and seasonal autoregressive integrated moving average (SARIMA)) and nonlinear (artificial neural network (ANN) and genetic programming (GP)) univariate models. Our results show that, while the SARIMA model is superior relative to other linear and nonlinear models, as it tends to produce smaller forecast errors; statistically, these forecasting gains are not significant relative to higher-order AR and nonlinear models, though simple benchmarks like the RW and AR(1) models are statistically outperformed. Overall, we show that in terms of forecasting the US CPI, accounting for nonlinearity does not necessarily provide us with any statistical gains." @default.
• W2313829901 created "2016-06-24" @default.
• W2313829901 creator A5000647243 @default.
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• W2313829901 date "2016-03-16" @default.
• W2313829901 modified "2023-10-10" @default.
• W2313829901 title "Forecasting US consumer price index: does nonlinearity matter?" @default.
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• W2313829901 doi "https://doi.org/10.1080/00036846.2016.1158922" @default.
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