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GDP Forecasting Bias due to Aggregation Inaccuracy in a Chain- Linking Framework

Author

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  • Marcus Cobb
Abstract
When evaluating the economy’s performance, Gross Domestic Product (GDP) is the most often used indicator and it is therefore also one of the most often forecasted. Due to the shortcomings of the traditional fixed-base methods, many countries have adopted chain-linking to avoid price structure obsolescence. This has meant that GDP’s well-known accounting identities hold only approximately raising challenges for those reading the numbers, but also for forecasters that follow approaches that rely on these accounting properties. Oddly enough, the issue of aggregation is hardly mentioned in forecasting. This omission could be the result of everybody adopting the chain-linking methodology with ease and considering it unnecessary to make a point out of it, but it could also originate from ignoring the issue altogether. Whatever the reason for this omission, it could lead practitioners that are unfamiliar with the method to make unnecessary mistakes. This document presents explicitly the role of prices in a bottom-up forecasting framework and, based on it, argues that they should be taken into account when generating aggregate forecasts based on the accounting identities. Also, something that should be taken into consideration by practitioners is that discrepancies due to aggregation inaccuracy are not necessarily negligible.

Suggested Citation

  • Marcus Cobb, 2014. "GDP Forecasting Bias due to Aggregation Inaccuracy in a Chain- Linking Framework," Working Papers Central Bank of Chile 721, Central Bank of Chile.
  • Handle: RePEc:chb:bcchwp:721
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    File URL: https://www.bcentral.cl/documents/33528/133326/DTBC_721.pdf
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    References listed on IDEAS

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    1. David F. Hendry & Kirstin Hubrich, 2011. "Combining Disaggregate Forecasts or Combining Disaggregate Information to Forecast an Aggregate," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(2), pages 216-227, April.
    2. Katja Heinisch & Rolf Scheufele, 2018. "Bottom-up or direct? Forecasting German GDP in a data-rich environment," Empirical Economics, Springer, vol. 54(2), pages 705-745, March.
    3. Esteves, Paulo Soares, 2013. "Direct vs bottom–up approach when forecasting GDP: Reconciling literature results with institutional practice," Economic Modelling, Elsevier, vol. 33(C), pages 416-420.
    4. Nikita Perevalov & Philipp Maier, 2010. "On the Advantages of Disaggregated Data: Insights from Forecasting the U.S. Economy in a Data-Rich Environment," Staff Working Papers 10-10, Bank of Canada.
    5. Alessandro Girardi & Roberto Golinelli & Carmine Pappalardo, 2017. "The role of indicator selection in nowcasting euro-area GDP in pseudo-real time," Empirical Economics, Springer, vol. 53(1), pages 79-99, August.
    6. Marcus Cobb, 2013. "Industry Contributions to GDP Quarterly Growth," Economic Statistics Series 100, Central Bank of Chile.
    7. Quilis, Enrique M., 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.
    8. Jorge Durán & Omar Licandro, 2012. "Is the GDP Growth Rate in NIPA a Welfare Measure?," Working Papers 665, Barcelona School of Economics.
    9. Hahn, Elke & Skudelny, Frauke, 2008. "Early estimates of euro area real GDP growth: a bottom up approach from the production side," Working Paper Series 975, European Central Bank.
    10. Joe Robjohns, 2007. "Methods explained: Contributions to growth rates under annual chain-linking," Economic & Labour Market Review, Palgrave Macmillan;Office for National Statistics, vol. 1(6), pages 53-56, June.
    11. Frank Smets & Raf Wouters, 2003. "An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area," Journal of the European Economic Association, MIT Press, vol. 1(5), pages 1123-1175, September.
    12. Pablo Burriel, 2012. "A real-time disaggregated forecasting model for euro area GDP," Economic Bulletin, Banco de España, issue APR, pages 93-103, April.
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    Cited by:

    1. Cobb, Marcus, 2014. "Explaining GDP Quarterly Growth from its Components in the Context of the Annual Overlap Method: A Comparison of Approaches," MPRA Paper 58022, University Library of Munich, Germany.
    2. Cobb, Marcus, 2014. "Identifying the Sources of Seasonal Effects in an indirectly adjusted Chain-Linked Aggregate: A Framework for the Annual Overlap Method," MPRA Paper 58033, University Library of Munich, Germany.

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