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Forecasting the US CPI: Does Nonlinearity Matter?

Marcos Álvarez-Díaz () and Rangan Gupta ()
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Marcos Álvarez-Díaz: Department of Economics, University of Vigo, Galicia, Spain

No 201512, Working Papers from University of Pretoria, Department of Economics

Abstract: The objective of this paper is to predict, both in-sample and out-of-sample, the consumer price index (CPI) of the United States (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 seasonally-adjusted autoregressive 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.

Keywords: Linear; Nonlinear; Forecasting; Consumer Price Index (search for similar items in EconPapers)
JEL-codes: C22 C45 C53 E31 (search for similar items in EconPapers)
Pages: 43 pages
Date: 2015-03
New Economics Papers: this item is included in nep-cmp, nep-for and nep-ore
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