Macroeconomic forecasting in the Euro area: Country specific versus area-wide information

Marcellino, Massimiliano; Stock, James H.; Watson, Mark W.

doi:10.1016/s0014-2921(02)00206-4

Cited by 325 publications

(283 citation statements)

References 8 publications

(11 reference statements)

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“…Although our methodology is similar to that of previous studies (see, inter alia, Marcellino et al, 2003), we implement a different procedure to generate the out-of-sample forecasts. More specifically, our out-of-sample forecasting exercise is organized in such a way to ensure that our benchmark model is always nested within the other estimated models.…”

Section: Econometric Methodologymentioning

confidence: 99%

“…[ Marcellino et al (2003) for their restricted group of EU countries. In contrast, pooled forecasts based on a simple average fare worst of all.…”

Section: -Step Ahead Forecastsmentioning

confidence: 99%

“…Marcellino et al (2003) have examined this issue with relation to the original 11 members 1 of the common currency group. However with the accession of ten new member states in 2004, the group has become even more diverse and hence forecasting economic variables becomes even more hazardous.…”

Section: Introductionmentioning

confidence: 99%

“…Banerjee and Marcellino (2006) conclude that simple forecasting mechanisms work best and note the relatively good performance of the pure autoregressive model. Furthermore, these relatively simple models have often been found to outperform multivariate models in their out-of-sample forecast accuracy, especially in periods of economic change (see Marcellino et al 2003). Given the recent period of economic and institutional change in European Union countries, especially the 10 new members, we favour the relatively low-parameterised single equation models.…”

Section: Introductionmentioning

confidence: 99%

“…We also investigate the forecast performance of the domestic money supply as well as extraneous data in the form of US aggregates of the variable to be forecast. Money supply growth has been used in both output and inflation forecasting exercises by Stock and Watson (2003), while the effect of US variables on their EU equivalents has been documented by Marcellino et al (2003).…”

Section: Introductionmentioning

confidence: 99%

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15. Some Policy Implications

2017

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We compare models for forecasting growth and inflation in the enlarged euro area. Forecasts are built from univariate autoregressive and single equation models. Aggregate forecasts are constructed by both employing aggregate variables and by aggregating country-specific forecasts. Using financial variables for country-specific forecasts tends to add little to the predictive ability of a simple AR model. However, they can help to predict EU aggregates. Furthermore, forecasts from pooling individual country models usually outperform those of the aggregate itself, particularly for EU25 grouping. This is particularly interesting from the perspective of the European Central Bank, who require forecasts of economic activity and inflation to formulate appropriate economic policy across the enlarged group.

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Section: Econometric Methodologymentioning

confidence: 99%

“…[ Marcellino et al (2003) for their restricted group of EU countries. In contrast, pooled forecasts based on a simple average fare worst of all.…”

Section: -Step Ahead Forecastsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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15. Some Policy Implications

2017

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Forecasting inflation using economic indicators: the case of France

Bruneau

Bandt

Flageollet

et al. 2007

Journal of Forecasting

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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 Â© 2007 John Wiley & Sons, Ltd.

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Forecasting with panel data

Baltagi

2008

Journal of Forecasting

454

249

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This paper gives a brief survey of forecasting with panel data. It begins with a simple error component regression model and surveys the best linear unbiased prediction under various assumptions of the disturbance term. This includes various ARMA models as well as spatial autoregressive models. The paper also surveys how these forecasts have been used in panel data applications, running horse races between heterogeneous and homogeneous panel data models using out-of-sample forecasts. Copyright © 2008 John Wiley & Sons, Ltd.

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Macroeconomic forecasting in the Euro area: Country specific versus area-wide information

Cited by 325 publications

References 8 publications

15. Some Policy Implications

15. Some Policy Implications

Forecasting inflation using economic indicators: the case of France

Forecasting with panel data

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