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Macroeconomic forecasting in the EMU: Does disaggregate modeling improve forecast accuracy?

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  • Ruth, Karsten

Abstract

Accurate forecasts of aggregate European variables are crucial for conducting a union-wide monetary policy. This paper investigates empirically whether pooling forecasts from disaggregate models is a promising strategy for forecasting actual macroeconomic European variables. In contrast to previous studies we formulate an intermediate case of disaggregation with regard to forecast combination by pooling forecasts obtained from models which are separately specified and estimated for subgroups of actual EMU Member States. Moreover, by modeling different degrees of monetary autonomy across countries during the EMS-era we explicitly account for cross-country heterogeneity in advance of 1999. We find that policymakers might obtain more accurate forecasts of actual European macroeconomic variables by pooling subgroup-specific forecasts compared to forecasting with a single union-wide model.

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  • Ruth, Karsten, 2008. "Macroeconomic forecasting in the EMU: Does disaggregate modeling improve forecast accuracy?," Journal of Policy Modeling, Elsevier, vol. 30(3), pages 417-429.
  • Handle: RePEc:eee:jpolmo:v:30:y:2008:i:3:p:417-429
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    Cited by:

    1. Antipa, Pamfili & Barhoumi, Karim & Brunhes-Lesage, Véronique & Darné, Olivier, 2012. "Nowcasting German GDP: A comparison of bridge and factor models," Journal of Policy Modeling, Elsevier, vol. 34(6), pages 864-878.
    2. Carrera, Cesar & Ledesma, Alan, 2015. "Proyección de la inflación agregada con modelos de vectores autorregresivos bayesianos," Working Papers 2015-003, Banco Central de Reserva del Perú.
    3. Mihaela Bratu, 2012. "A Strategy to Improve the Survey of Professional Forecasters (SPF) Predictions Using Bias-Corrected-Accelerated (BCA) Bootstrap Forecast Intervals," International Journal of Synergy and Research, ToKnowPress, vol. 1(2), pages 45-59.
    4. Cesar Carrera & Alan Ledesma, 2015. "Aggregate Inflation Forecast with Bayesian Vector Autoregressive Models," Working Papers 2015-50, Peruvian Economic Association.
    5. Mihaela Bratu (Simionescu), 2013. "How to Improve the SPF Forecasts?," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 9(2), pages 153-165, April.
    6. Constantin Mitru? & Mihaela Bratu (Simionescu), 2013. "The Indicators’ Inadequacy and the Predictions’ Accuracy," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 9(4), pages 430-442, August.
    7. BRATU SIMIONESCU, Mihaela, 2012. "Two Quantitative Forecasting Methods For Macroeconomic Indicators In Czech Republic," Annals of Spiru Haret University, Economic Series, Universitatea Spiru Haret, vol. 3(1), pages 71-87.
    8. Mihaela BRATU (SIMIONESCU), 2012. "A Strategy To Improve The Gdp Index Forcasts In Romania Using Moving Average Models Of Historical Errors Of The Dobrescu Macromodel," Romanian Journal of Economics, Institute of National Economy, vol. 35(2(44)), pages 128-138, December.
    9. Bratu Mihaela, 2013. "An Evaluation Of Usa Unemployment Rate Forecasts In Terms Of Accuracy And Bias. Empirical Methods To Improve The Forecasts Accuracy," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 1, pages 170-180, February.
    10. Mihaela Simionescu, 2015. "The Improvement of Unemployment Rate Predictions Accuracy," Prague Economic Papers, University of Economics, Prague, vol. 2015(3), pages 274-286.

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