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.