IDEAS home Printed from https://ideas.repec.org/p/cte/wsrepe/30899.html
   My bibliography  Save this paper

Discovering general and sectorial trends in a large set of time series

Author

Listed:
  • Carlomagno Real, Guillermo
  • Espasa, Antoni

Abstract

The objective of this research note is to extend the pairwise procedure studied by Car- lomagno and Espasa (ming) to the case of general and sectorial trends. The extension allows to discover subsets of series that share general and/or sectorial stochastic trends between a (possible large) set of time series. This could be useful to model and forecast all of the series under analysis. Our approach does not need to assume pervasiveness of the trends, nor impose special restrictions on the serial or cross-sectional idiosyncratic correlation of the series. Additionally, the asymptotic theory works both, with finite N and T ! 1, and with [T;N] ! 1. In a Monte Carlo experiment we show that the extended procedure can produce reliable results in finite samples.

Suggested Citation

  • Carlomagno Real, Guillermo & Espasa, Antoni, 2020. "Discovering general and sectorial trends in a large set of time series," DES - Working Papers. Statistics and Econometrics. WS 30899, Universidad Carlos III de Madrid. Departamento de Estadística.
    • Handle: RePEc:cte:wsrepe:30899
    as

    Download full text from publisher

    File URL: https://e-archivo.uc3m.es/bitstream/handle/10016/30899/ws202007.pdf?sequence=1
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    Cointegration;

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • 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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:cte:wsrepe:30899. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ana Poveda (email available below). General contact details of provider: http://portal.uc3m.es/portal/page/portal/dpto_estadistica .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.