Forecasting Inflation using Economic Indicators: the Case of France

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. It turns out that, according to usual statistical criteria, the combination of several indicators -in particular those derived from surveys- provides better results than dynamic 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 HICP excluding unprocessed food and energy are very encouraging. Moreover, we show that it is possible to use forecasts on this indicator to project overall inflation.Inflation ; Out-of-sample forecast ; Indicator models ; Dynamic factor models ; Phillips curve.

Measuring co-movements in the Euro area using a nonstationary factor model

This article investigates to what extent business cycles co-move in the four largest euro area economies, using a large-scale database of nonstationary series for the euro area over the 1980:Q1 to 2003:Q4 period. We apply the methodology proposed by Bai (2004) and Bai and Ng (2004) to construct a coincident indicator of the euro area business cycle, based on the first common factor estimated from a dynamic factor analysis on the level of the variables. The indicator appears to be significantly close, from a statistical point of view, to the level of the euro area GDP in the most recent period. We also show that national developments are increasingly correlated to the indicator at the business cycle frequencies. We finally suggest a decomposition of GDP growth along the different stationary and nonstationary factors.

Forecasting inflation using economic indicators: the case of France

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.