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Forecasting monthly us consumer price indexes through a disaggregated I(2) analysis

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  • Espasa, Antoni
  • Poncela, Pilar
  • Senra, Eva

Abstract

In this paper we carry a disaggregated study of the monthly US Consumer Price Index (CPI). We consider a breakdown of US CPI in four subindexes, corresponding to four groups of markets: energy, food, rest of commodities and rest of services. This is seen as a relevant way to increase information in forecasting US CPI because the supplies and demands in those markets have very different characteristics. Consumer prices in the last three components show I(2) behavior, while the energy subindex shows a lower order of integration, but with segmentation in the growth rate. Even restricting the analysis to the series that show the same order of integration, the trending behavior of prices in these markets can be very different. An I(2) cointegration analysis on the mentioned last three components shows that there are several sources of nonstationarity in the US CPI components. A common trend analysis based on dynamic factor models confirms these results. The different trending behavior in the market prices suggests that theories for price determinations could differ through markets. In this context, disaggregation could help to improve forecasting accuracy. To show that this conjecture is valid for the non-energy US CPI, we have performed a forecasting exercise of each component, computed afterwards the aggregated value of the non energy US CPI and compared it with the forecasts obtained directly from a model for the aggregate. The improvement in one year ahead forecasts with the disaggregated approach is more than 20%, where the root mean squared error is employed as a measure of forecasting performance.

Suggested Citation

  • Espasa, Antoni & Poncela, Pilar & Senra, Eva, 2002. "Forecasting monthly us consumer price indexes through a disaggregated I(2) analysis," DES - Working Papers. Statistics and Econometrics. WS ws020301, Universidad Carlos III de Madrid. Departamento de Estadística.
  • Handle: RePEc:cte:wsrepe:ws020301
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    1. P. C. B. Phillips & S. N. Durlauf, 1986. "Multiple Time Series Regression with Integrated Processes," Review of Economic Studies, Oxford University Press, vol. 53(4), pages 473-495.
    2. Tor Jacobson & Per Jansson & Anders Vredin & Anders Warne, 2001. "Monetary policy analysis and inflation targeting in a small open economy: a VAR approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(4), pages 487-520.
    3. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    4. Bidarkota, Prasad V, 2001. "Alternative Regime Switching Models for Forecasting Inflation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(1), pages 21-35, January.
    5. Philip Hans Franses, 2001. "How to deal with intercept and trend in practical cointegration analysis?," Applied Economics, Taylor & Francis Journals, vol. 33(5), pages 577-579.
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    Cited by:

    1. Janine Aron & John Muellbauer & Coen Pretorius, 2004. "A Framework for Forecasting the Components of the Consumer Price," Development and Comp Systems 0409054, EconWPA.
    2. Janine Aron & John Muellbauer, 2008. "New methods for forecasting inflation and its sub-components: application to the USA," Economics Series Working Papers 406, University of Oxford, Department of Economics.
    3. Tallman, Ellis W. & Zaman, Saeed, 2017. "Forecasting inflation: Phillips curve effects on services price measures," International Journal of Forecasting, Elsevier, pages 442-457.
    4. Aron, Janine & Muellbauer, John, 2012. "Improving forecasting in an emerging economy, South Africa: Changing trends, long run restrictions and disaggregation," International Journal of Forecasting, Elsevier, vol. 28(2), pages 456-476.
    5. Aron, Janine & Muellbauer, John, 2010. "Does aggregating forecasts by CPI component improve inflation forecast accuracy in South Africa?," CEPR Discussion Papers 7895, C.E.P.R. Discussion Papers.

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