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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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    1. repec:spr:empeco:v:53:y:2017:i:1:d:10.1007_s00181-016-1158-5 is not listed on IDEAS
    2. Tony Chernis & Calista Cheung & Gabriella Velasco, 2017. "A Three-Frequency Dynamic Factor Model for Nowcasting Canadian Provincial GDP Growth," Discussion Papers 17-8, Bank of Canada.
    3. repec:spr:empeco:v:53:y:2017:i:1:d:10.1007_s00181-017-1254-1 is not listed on IDEAS
    4. Alain Galli & Christian Hepenstrick & Rolf Scheufele, 2017. "Mixed-frequency models for tracking short-term economic developments in Switzerland," Working Papers 2017-02, Swiss National Bank.
    5. André Binette & Tony Chernis & Daniel de Munnik, 2017. "Global Real Activity for Canadian Exports: GRACE," Discussion Papers 17-2, Bank of Canada.
    6. Francisco Corona & Pilar Poncela & Esther Ruiz, 2017. "Determining the number of factors after stationary univariate transformations," Empirical Economics, Springer, vol. 53(1), pages 351-372, August.
    7. 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.
    8. Ruiz Ortega, Esther & Maldonado, Javier, 2017. "Accurate Subsampling Intervals of Principal Components Factors," DES - Working Papers. Statistics and Econometrics. WS 23974, Universidad Carlos III de Madrid. Departamento de Estadística.
    9. Tony Chernis & Rodrigo Sekkel, 2017. "A dynamic factor model for nowcasting Canadian GDP growth," Empirical Economics, Springer, vol. 53(1), pages 217-234, August.

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    Keywords

    Business cycles; Output growth; Time series;

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