Do disaggregated CPI data improve the accuracy of inflation forecasts?
Economic Modelling, 2012, vol. 29, issue 4, 1305-1313
In this paper, we evaluate the role of using consumer price index (CPI) disaggregated data to improve the accuracy of inflation forecasts. Our forecasting approach is based on extracting the factors from the subcomponents of the CPI at the highest degree of disaggregation. The data set contains 54 macroeconomic series and 243 CPI subcomponents from 1992 to 2009 for Mexico. We find that the factor models that include disaggregated data outperform the benchmark autoregressive model and the factor models containing alternative groups of macroeconomic variables. We provide evidence that using disaggregated price data improves forecasting performance. The forecasts of the factor models that extract the information from the CPI disaggregated data are as accurate as the forecasts from the survey of experts.
Keywords: Factor models; Inflation forecasting; Disaggregate information; Principal components; Forecast evaluation (search for similar items in EconPapers)
JEL-codes: C22 C53 E37 (search for similar items in EconPapers)
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Working Paper: Forecasting Inflation in Mexico Using Factor Models: Do Disaggregated CPI Data Improve Forecast Accuracy? (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:29:y:2012:i:4:p:1305-1313
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