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Spectral Analysis: Time Series Analysis in Frequency Domain

Listed author(s):
  • Vishwanathan Iyer
  • Kaushik Roy Chowdhury
Registered author(s):

    : 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.

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    Article provided by IUP Publications in its journal The IUP Journal of Applied Economics.

    Volume (Year): VIII (2009)
    Issue (Month): 5-6 (September-November)
    Pages: 83-101

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    Handle: RePEc:icf:icfjae:v:08:y:2009:i:5-6:p:83-101
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