Curtin University Homepage
  • Library
  • Help
    • Admin

    espace - Curtin’s institutional repository

    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item

    Frequency Domain

    Access Status
    Fulltext not available
    Authors
    Chan, Felix
    Reale, M.
    Date
    2020
    Type
    Book Chapter
    
    Metadata
    Show full item record
    Citation
    Chan, F. and Reale, M. 2019. Frequency Domain, in Fuleky, P. (ed), Macroeconomic Forecasting in the Era of Big Data, Advanced Studies in Theoretical and Applied Econometrics, vol. 52, pp. 655-687. Cham, Switzerland: Springer.
    Source Title
    Advanced Studies in Theoretical and Applied Econometrics
    DOI
    10.1007/978-3-030-31150-6_20
    Faculty
    Faculty of Business and Law
    School
    School of Economics, Finance and Property
    URI
    http://hdl.handle.net/20.500.11937/77778
    Collection
    • Curtin Research Publications
    Abstract

    Time series analysis in frequency domain has always been an active area of research. Theorists often employ techniques in frequency domain to advance current understanding on complex time series properties and develop useful toolboxes for practical time series analysis. This chapter reviews several concepts from frequency domain that are helpful for forecasting. The main focus is on the intuition behind these techniques rather than a rigorous mathematical introduction. In addition to the traditional frequency domain techniques, this chapter also discusses a time-frequency domain technique called wavelets, which has recently become an active area of research in financial econometrics due to the availability of tall and huge financial data. A novel application of the ZVAR model based on the generalised shift operator will also be introduced. ZVAR has the ability to produce forecasts at a sampling frequency that is different from the sampling frequency of the data. Monte Carlo experiments show that this approach performs relatively well when compared with the forecast performance of the true data generating process. Given the availabilities of big data, one may expect data with different sampling frequencies would become more common. ZVAR would seem to be a complementary method to other mixed frequency approaches such as Mixed Data Sampling (MIDAS).

    Related items

    Showing items related by title, author, creator and subject.

    • Multi fault diagnosis based on loading matrix and score matrix of principal component analysis for a centrifugal pump
      Kamiel, B.; Howard, Ian (2014)
      Centrifugal pumps are one of the rotating machines that are widely used in various industries such as oil and gas, petrochemical, water treatment, power generation, agriculture, and fertilizers. During its operation, it ...
    • Low order channel estimation for CDMA systems
      Abd El-Sallam, Amar (2005)
      New approaches and algorithms are developed for the identification and estimation of low order models that represent multipath channel effects in Code Division Multiple Access (CDMA) communication systems. Based on these ...
    • An investigation into active and passive acoustic techniques to study aggregating fish species
      Parsons, Miles James Gerard (2009)
      Techniques of single- and multi-beam active acoustics and the passive recording of fish vocalisations were employed to evaluate the benefits and limitations of each technique as a method for assessing and monitoring fish ...
    Advanced search

    Browse

    Communities & CollectionsIssue DateAuthorTitleSubjectDocument TypeThis CollectionIssue DateAuthorTitleSubjectDocument Type

    My Account

    Admin

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Follow Curtin

    • 
    • 
    • 
    • 
    • 

    CRICOS Provider Code: 00301JABN: 99 143 842 569TEQSA: PRV12158

    Copyright | Disclaimer | Privacy statement | Accessibility

    Curtin would like to pay respect to the Aboriginal and Torres Strait Islander members of our community by acknowledging the traditional owners of the land on which the Perth campus is located, the Whadjuk people of the Nyungar Nation; and on our Kalgoorlie campus, the Wongutha people of the North-Eastern Goldfields.