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Forecasting aggregates and disaggregates with common features

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  • Espasa, Antoni
  • Mayo-Burgos, Iván

Abstract

This paper focuses on the provision of consistent forecasts for an aggregate economic indicator, such as a consumer price index and its components. The procedure developed is a disaggregated approach based on single-equation models for the components, which take into account the stable features that some components share, such as a common trend and common serial correlation. Our procedure starts by classifying a large number of components based on restrictions from common features. The result of this classification is a disaggregation map, which may also be useful in applying dynamic factors, defining intermediate aggregates or formulating models with unobserved components. We use the procedure to forecast inflation in the Euro area, the UK and the US. Our forecasts are significantly more accurate than either a direct forecast of the aggregate or various other indirect forecasts.

Suggested Citation

  • Espasa, Antoni & Mayo-Burgos, Iván, 2013. "Forecasting aggregates and disaggregates with common features," International Journal of Forecasting, Elsevier, vol. 29(4), pages 718-732.
    • Handle: RePEc:eee:intfor:v:29:y:2013:i:4:p:718-732
    DOI: 10.1016/j.ijforecast.2012.10.004
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    Cited by:

    1. Carlomagno, Guillermo & Espasa, Antoni, 2014. "The pairwise approach to model a large set of disaggregates with common trends," DES - Working Papers. Statistics and Econometrics. WS ws141309, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. Cobb, Marcus P A, 2017. "Joint Forecast Combination of Macroeconomic Aggregates and Their Components," MPRA Paper 76556, University Library of Munich, Germany.
    3. Guillermo Carlomagno & Nicolas Eterovic & L. G. Hernández-Román, 2023. "Disentangling Demand and Supply Inflation Shocks from Chilean Electronic Payment Data," Working Papers Central Bank of Chile 986, Central Bank of Chile.
    4. Jennifer L. Castle & David F. Hendry, 2010. "Nowcasting from disaggregates in the face of location shifts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 200-214.
    5. Chalmovianský, Jakub & Porqueddu, Mario & Sokol, Andrej, 2020. "Weigh(t)ing the basket: aggregate and component-based inflation forecasts for the euro area," Working Paper Series 2501, European Central Bank.
    6. Carlomagno, Guillermo & Espasa, Antoni, 2015. "Forecasting a large set of disaggregates with common trends and outliers," DES - Working Papers. Statistics and Econometrics. WS ws1518, Universidad Carlos III de Madrid. Departamento de Estadística.
    7. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2013. "Model Selection in Equations with Many ‘Small’ Effects," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(1), pages 6-22, February.
    8. Cobb, Marcus P A, 2017. "Forecasting Economic Aggregates Using Dynamic Component Grouping," MPRA Paper 81585, University Library of Munich, Germany.
    9. Bujosa, Marcos & García-Hiernaux, Alfredo, 2013. "Some considerations about “Forecasting aggregates and disaggregates with common features”," International Journal of Forecasting, Elsevier, vol. 29(4), pages 733-735.
    10. Carlomagno Real, Guillermo & Espasa, Antoni, 2017. "Discovering pervasive and non-pervasive common cycles," DES - Working Papers. Statistics and Econometrics. WS 25392, Universidad Carlos III de Madrid. Departamento de Estadística.
    11. Pino, Gabriel & Tena Horrillo, Juan de Dios & Espasa, Antoni, 2013. "Forecasting disaggregates by sectors and regions : the case of inflation in the euro area and Spain," DES - Working Papers. Statistics and Econometrics. WS ws130807, Universidad Carlos III de Madrid. Departamento de Estadística.
    12. Cobb, Marcus P A, 2018. "Improving Underlying Scenarios for Aggregate Forecasts: A Multi-level Combination Approach," MPRA Paper 88593, University Library of Munich, Germany.
    13. César Castro & Rebeca Jiménez-Rodríguez & Pilar Poncela & Eva Senra, 2017. "A new look at oil price pass-through into inflation: evidence from disaggregated European data," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 34(1), pages 55-82, April.
    14. Gabriel Pino & J. D. Tena & Antoni Espasa, 2016. "Geographical disaggregation of sectoral inflation. Econometric modelling of the Euro area and Spanish economies," Applied Economics, Taylor & Francis Journals, vol. 48(9), pages 799-815, February.
    15. Carlomagno, Guillermo & Espasa, Antoni, 2016. "Discovering common trends in a large set of disaggregates: statistical procedures and their properties," DES - Working Papers. Statistics and Econometrics. WS ws1519, Universidad Carlos III de Madrid. Departamento de Estadística.
    16. Eliana R. González-Molano & Ramón Hernández-Ortega & Edgar Caicedo-García & Nicolás Martínez-Cortés & Jose Vicente Romero & Anderson Grajales-Olarte, 2020. "Nueva Clasificación del BANREP de la Canasta del IPC y revisión de las medidas de Inflación Básica en Colombia," Borradores de Economia 1122, Banco de la Republica de Colombia.
    17. Espasa, Antoni & Senra, Eva, 2017. "22 Years of inflation assessment and forecasting experience at the bulletin of EU & US inflation and macroeconomic analysis," DES - Working Papers. Statistics and Econometrics. WS 24678, Universidad Carlos III de Madrid. Departamento de Estadística.
    18. Guillermo Carlomagno & Antoni Espasa, 2021. "Discovering Specific Common Trends in a Large Set of Disaggregates: Statistical Procedures, their Properties and an Empirical Application," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(3), pages 641-662, June.
    19. Cuevas Rumín, Ángel & Quilis, Enrique M. & Espasa, Antoni, 2011. "Combining benchmarking and chain-linking for short-term regional forecasting," DES - Working Papers. Statistics and Econometrics. WS ws114130, Universidad Carlos III de Madrid. Departamento de Estadística.
    20. Jennifer Castle & David Hendry & Oleg Kitov, 2013. "Forecasting and Nowcasting Macroeconomic Variables: A Methodological Overview," Economics Series Working Papers 674, University of Oxford, Department of Economics.
    21. Antoni Espasa & Eva Senra, 2017. "Twenty-Two Years of Inflation Assessment and Forecasting Experience at the Bulletin of EU & US Inflation and Macroeconomic Analysis," Econometrics, MDPI, vol. 5(4), pages 1-28, October.
    22. Cobb, Marcus P A, 2017. "Aggregate Density Forecasting from Disaggregate Components Using Large VARs," MPRA Paper 76849, University Library of Munich, Germany.

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