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Forecasting the US CPI: Does Nonlinearity Matter?

Author

Listed:
  • Marcos Álvarez-Díaz

    (Department of Economics, University of Vigo, Galicia, Spain)

  • Rangan Gupta

    (Department of Economics, University of Pretoria)

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.

Suggested Citation

  • Marcos Álvarez-Díaz & Rangan Gupta, 2015. "Forecasting the US CPI: Does Nonlinearity Matter?," Working Papers 201512, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:201512
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    Citations

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    Cited by:

    1. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou & Rangan Gupta, 2017. "The Informational Content of the Term Spread in Forecasting the US Inflation Rate: A Nonlinear Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(2), pages 109-121, March.
    2. Nyoni, Thabani, 2019. "Modeling and forecasting CPI in Myanmar: An application of ARIMA models," MPRA Paper 92420, University Library of Munich, Germany.
    3. Nyoni, Thabani, 2019. "Analyzing CPI dynamics in Italy," MPRA Paper 92421, University Library of Munich, Germany.
    4. Denis Shibitov & Mariam Mamedli, 2021. "Forecasting Russian Cpi With Data Vintages And Machine Learning Techniques," Bank of Russia Working Paper Series wps70, Bank of Russia.
    5. Nyoni, Thabani, 2019. "Predicting consumer price index in Saudi Arabia," MPRA Paper 92422, University Library of Munich, Germany.
    6. Nyoni, Thabani, 2019. "Modeling and forecasting CPI in Iran: A univariate analysis," MPRA Paper 92454, University Library of Munich, Germany.
    7. Nyoni, Thabani, 2019. "Modeling and forecasting CPI in Mauritius," MPRA Paper 92423, University Library of Munich, Germany.

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    More about this item

    Keywords

    Linear; Nonlinear; Forecasting; Consumer Price Index;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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