IDEAS home Printed from https://ideas.repec.org/p/ptu/wpaper/w200913.html
   My bibliography  Save this paper

Dynamic factor models with jagged edge panel data: Taking on board the dynamics of the idiosyncratic components

Author

Listed:
  • Maximiano Pinheiro
  • António Rua
  • Francisco Craveiro Dias

Abstract

The estimation of dynamic factor models for large cross-sections poses a challenge in a real time environment. As macroeconomic data become available with different delays, unbalanced panel data sets with missing values at the end of the sample period (the so-called "jagged edge") have to be handled when estimating the factor model. In this paper, we propose an EM algorithm which copes with such data sets, accounts for autoregressive common factors and allows for serial correlation in the idiosyncratic components. Based on Monte Carlo simulations, we find that taking on board the dynamics of the idiosyncratic components improves significantly the accuracy of the estimation of both the missing values and the common factors at the end of the sample period.

Suggested Citation

  • Maximiano Pinheiro & António Rua & Francisco Craveiro Dias, 2009. "Dynamic factor models with jagged edge panel data: Taking on board the dynamics of the idiosyncratic components," Working Papers w200913, Banco de Portugal, Economics and Research Department.
  • Handle: RePEc:ptu:wpaper:w200913
    as

    Download full text from publisher

    File URL: https://www.bportugal.pt/sites/default/files/anexos/papers/wp200913.pdf
    Download Restriction: no

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. repec:spr:empeco:v:53:y:2017:i:1:d:10.1007_s00181-016-1158-5 is not listed on IDEAS
    2. repec:eme:aecozz:s0731-905320150000035010 is not listed on IDEAS
    3. António Rua, 2016. "A wavelet-based multivariate multiscale approach for forecasting," Working Papers w201612, Banco de Portugal, Economics and Research Department.
    4. repec:eee:intfor:v:33:y:2017:i:3:p:581-590 is not listed on IDEAS
    5. 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.
    6. Francisco Craveiro Dias & Maximiano Pinheiro & António Rua, 2014. "Forecasting Portuguese GDP with factor models," Economic Bulletin and Financial Stability Report Articles, Banco de Portugal, Economics and Research Department.
    7. 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.
    8. Pilar Poncela & Esther Ruiz, 2016. "Small- Versus Big-Data Factor Extraction in Dynamic Factor Models: An Empirical Assessment," Advances in Econometrics,in: Dynamic Factor Models, volume 35, pages 401-434 Emerald Publishing Ltd.
    9. Dias, Francisco & Pinheiro, Maximiano & Rua, António, 2015. "Forecasting Portuguese GDP with factor models: Pre- and post-crisis evidence," Economic Modelling, Elsevier, vol. 44(C), pages 266-272.

    More about this item

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ptu:wpaper:w200913. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (DEE-NTDD). General contact details of provider: http://edirc.repec.org/data/bdpgvpt.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.