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Stationarity Test for Aggregate Outputs in the Presence of Structural Breaks


  • D.K. Srivastava

    () (Madras School of Economics)

  • K.R. Shanmugam

    () (Madras School of Economics)


This study tests for the stationarity of aggregate output (GDP at factor cost) and its three major components, namely GDP agriculture, GDP industry and GDP services in the presence of structural breaks during 1950-51 to 2011-12. Results indicate that (i) the GDP has three break points; (ii) GDP agriculture contains one while the GDP industry and GDP services contain four breaks each; and (iii) all variables are trends stationary with one or more structural breaks. Our alternative test, which tests the null of unit root for the study variables after removing the effects of trend and structural breaks, also confirms that the aggregate output variables are trend stationary with structural breaks. We also compare the identified structural break dates with earlier studies.

Suggested Citation

  • D.K. Srivastava & K.R. Shanmugam, 2012. "Stationarity Test for Aggregate Outputs in the Presence of Structural Breaks," Working Papers 2012-072, Madras School of Economics,Chennai,India.
  • Handle: RePEc:mad:wpaper:2012-072

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    Cited by:

    1. Chakravartti, Parma & Mundle, Sudipto, 2017. "An Automatic Leading Indicator Based Growth Forecast For 2016-17 and The Outlook Beyond," Working Papers 17/193, National Institute of Public Finance and Policy.
    2. Parma Chakravartti & Sudipto Mundle, 2017. "An Automatic Leading Indicator Based Growth Forecast For 2016-17 and The Outlook Beyond," Working Papers id:11773, eSocialSciences.

    More about this item


    Structural breaks; Indian economy; Time series; Stationarity test;

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes


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