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Nowcasting an Economic Aggregate with Disaggregate Dynamic Factors: An Application to Portuguese GDP

Author

Listed:
  • António José Morgado

    (GEE, Ministério da Economia e da Inovação; Faculdade de Economia, Universidade Nova de Lisboa)

  • Luis Catela Nunes

    (Faculdade de Economia, Universidade Nova de Lisboa)

  • Susana Salvado

    (GEE, Ministério da Economia e da Inovação; Faculdade de Economia, Universidade Nova de Lisboa)

Abstract

This paper consists of an empirical study comparing a dynamic factor model approach to estimate the current quarter aggregate GDP with the alternative approach of aggregating the forecasts obtained from specific dynamic factor models for each major expenditure disaggregate. The out-of-sample forecasting performance results suggest that there is no advantage in aggregating the disaggregate forecasts.

Suggested Citation

  • António José Morgado & Luis Catela Nunes & Susana Salvado, 2007. "Nowcasting an Economic Aggregate with Disaggregate Dynamic Factors: An Application to Portuguese GDP," GEE Papers 0002, Gabinete de Estratégia e Estudos, Ministério da Economia, revised Feb 2007.
    • Handle: RePEc:mde:wpaper:0002
    as

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    File URL: https://www.gee.gov.pt/RePEc/WorkingPapers/GEE_PAPERS_2.pdf
    File Function: First version, 2007
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    Citations

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    Cited by:

    1. Hopp Daniel, 2022. "Economic Nowcasting with Long Short-Term Memory Artificial Neural Networks (LSTM)," Journal of Official Statistics, Sciendo, vol. 38(3), pages 847-873, September.
    2. Daniel Hopp, 2022. "Benchmarking Econometric and Machine Learning Methodologies in Nowcasting," Papers 2205.03318, arXiv.org.
    3. Daniel Hopp, 2021. "Economic Nowcasting with Long Short-Term Memory Artificial Neural Networks (LSTM)," Papers 2106.08901, arXiv.org.
    4. Daniel Hopp, 2022. "Performance of long short-term memory artificial neural networks in nowcasting during the COVID-19 crisis," Papers 2203.11872, arXiv.org.

    More about this item

    Keywords

    Forecasting; Dynamic Factor Model; Temporal Disaggregation;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access

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