Forecasting Monthly Us Consumer Price Indexes Through A Disaggregated I(2) Analysis
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.
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- 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.
- Phillips, P C B & Durlauf, S N, 1986.
"Multiple Time Series Regression with Integrated Processes,"
Review of Economic Studies,
Wiley Blackwell, vol. 53(4), pages 473-95, August.
- Peter C.B. Phillips & Steven N. Durlauf, 1985. "Multiple Time Series Regression with Integrated Processes," Cowles Foundation Discussion Papers 768, Cowles Foundation for Research in Economics, Yale University.
- Franses, Ph.H.B.F., 1999.
"How to deal with intercept and trend in pratical cointegration analysis?,"
Econometric Institute Research Papers
EI 9904-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
- 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.
- 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 1057-72, June.
- 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.
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