Forecasting inflation using economic indicators: the case of France

Abstract

In order to provide short-run forecasts of headline and core HICP inflation for France, we assess the forecasting performance of a large set of economic indicators, individually and jointly, as well as using dynamic factor models. We run out-of-sample forecasts implementing the Stock and Watson (1999) methodology. We find that, according to usual statistical criteria, the combination of several indicators-in particular those derived from surveys-provides better results than factor models, even after pre-selection of the variables included in the panel. However, factors included in VAR models exhibit more stable forecasting performance over time. Results for the HICP excluding unprocessed food and energy are very encouraging. Moreover, we show that the aggregation of forecasts on subcomponents exhibits the best performance for projecting total inflation and that it is robust to data snooping. Copyright © 2007 John Wiley & Sons, Ltd.

Forecasting Inflation Rates Using Daily Data: A Nonparametric MIDAS Approach

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Forecasting Inflation Across Euro Area Countries and Sectors: A Panel VAR Approach

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“…Regarding the second dimension, country‐specific studies by Bruneau et al. () and Duarte and Rua () and Moser et al. () all find that aggregating HICP component forecasts yields more accurate predictions of inflation in France, Austria and Portugal, respectively.…”

Forecasting automobile sales: the Peña-Box approach

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