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Spectral Analysis: Time Series Analysis in Frequency Domain

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  • Vishwanathan Iyer
  • Kaushik Roy Chowdhury

Abstract

: This paper describes how the frequency domain analysis provides an alternative approach to time domain analysis of a given time series. Spectral and periodogram analyses of a given time series are performed to detect trends and seasonalities in the data. A cross-spectral analysis is done to find causality and comovements in two different time series. Univariate frequency domain analysis is done using time series of varying nature including simulated white noise process, random walk process, AR(1) process, Wolfer s Sunspot data and Box-Jenkins Airlines data; while bivariate (cross-spectral) analysis is done for macroeconomic variables such as money in circulation and inflation.

Suggested Citation

  • Vishwanathan Iyer & Kaushik Roy Chowdhury, 2009. "Spectral Analysis: Time Series Analysis in Frequency Domain," The IUP Journal of Applied Economics, IUP Publications, vol. 0(5-6), pages 83-101, September.
    • Handle: RePEc:icf:icfjae:v:08:y:2009:i:5-6:p:83-101
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    Cited by:

    1. Sara Ariza-Murillo & Fredy Gamboa-Estrada & Camilo Andrés Orozco-Vanegas, 2023. "El impacto potencial de los movimientos de portafolio de los inversionistas extranjeros sobre la tasa de cambio en Colombia," Borradores de Economia 1261, Banco de la Republica de Colombia.

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