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Aggregate versus disaggregate information in dynamic factor models

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  • Alvarez, Rocio
  • Camacho, Maximo
  • Perez-Quiros, Gabriel

Abstract

We examine the finite-sample performances of dynamic factor models that use either aggregate or disaggregate data, where the latter rely on finer disaggregations of the headline concepts of a small set of economic categories. Our Monte Carlo analysis reveals that the use of the series with the largest averaged within-category correlations outperforms the use of disaggregate data for factor estimation and forecasting in several cases. This occurs for high levels of cross-correlation across the idiosyncratic errors of series that belong to the same category, for oversampled categories, and especially for high levels of persistence in either the common factor or the idiosyncratic errors. However, the forecasting gains are reduced considerably when the target series are persistent. This could potentially explain why there is no clear ranking between the aggregate and disaggregate approaches when using the constituent balanced panel of the Stock-Watson factor model, which classifies the US data into 13 economic categories.

Suggested Citation

  • Alvarez, Rocio & Camacho, Maximo & Perez-Quiros, Gabriel, 2016. "Aggregate versus disaggregate information in dynamic factor models," International Journal of Forecasting, Elsevier, vol. 32(3), pages 680-694.
    • Handle: RePEc:eee:intfor:v:32:y:2016:i:3:p:680-694
    DOI: 10.1016/j.ijforecast.2015.10.006
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    3. Tony Chernis & Rodrigo Sekkel, 2017. "A dynamic factor model for nowcasting Canadian GDP growth," Empirical Economics, Springer, vol. 53(1), pages 217-234, August.
    4. Marcos Bujosa & Antonio García‐Ferrer & Aránzazu de Juan & Antonio Martín‐Arroyo, 2020. "Evaluating early warning and coincident indicators of business cycles using smooth trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 1-17, January.
    5. Gabe de Bondt & Arne Gieseck & Pablo Herrero & Zivile Zekaite, 2021. "Euro Area Income and Wealth Effects: Aggregation Issues," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(6), pages 1454-1474, December.
    6. Alain Galli & Christian Hepenstrick & Rolf Scheufele, 2019. "Mixed-Frequency Models for Tracking Short-Term Economic Developments in Switzerland," International Journal of Central Banking, International Journal of Central Banking, vol. 15(2), pages 151-178, June.
    7. Karen Poghosyan & Ruben Poghosyan, 2021. "On the Applicability of Dynamic Factor Models for Forecasting Real GDP Growth in Armenia," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 71(1), pages 52-79, June.
    8. André Binette & Tony Chernis & Daniel de Munnik, 2017. "Global Real Activity for Canadian Exports: GRACE," Discussion Papers 17-2, Bank of Canada.
    9. Proietti, Tommaso & Giovannelli, Alessandro & Ricchi, Ottavio & Citton, Ambra & Tegami, Christían & Tinti, Cristina, 2021. "Nowcasting GDP and its components in a data-rich environment: The merits of the indirect approach," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1376-1398.
    10. Alain Galli, 2018. "Which Indicators Matter? Analyzing the Swiss Business Cycle Using a Large-Scale Mixed-Frequency Dynamic Factor Model," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 14(2), pages 179-218, November.
    11. Poncela, Pilar, 2021. "Dynamic factor models: does the specification matter?," DES - Working Papers. Statistics and Econometrics. WS 32210, Universidad Carlos III de Madrid. Departamento de Estadística.
    12. Karen Miranda & Pilar Poncela & Esther Ruiz, 2022. "Dynamic factor models: Does the specification matter?," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 13(1), pages 397-428, May.
    13. repec:cte:wsrepe:23974 is not listed on IDEAS
    14. Hauber, Philipp, 2022. "Real-time nowcasting with sparse factor models," EconStor Preprints 251551, ZBW - Leibniz Information Centre for Economics.
    15. Robert Lehmann & Magnus Reif & Timo Wollmershäuser, 2020. "ifoCAST: Der neue Prognosestandard des ifo Instituts," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 73(11), pages 31-39, November.
    16. Christian Garciga & Randal J. Verbrugge & Saeed Zaman, 2024. "The Effect of Component Disaggregation on Measures of the Median and Trimmed-Mean CPI," Working Papers 24-02, Federal Reserve Bank of Cleveland.

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