IDEAS home Printed from https://ideas.repec.org/p/cde/cdewps/162.html
   My bibliography  Save this paper

Modelling Seasonal Dynamics in Indian Industrial Production--An Extention of TV-STAR Model

Author

Listed:
  • Pami Dua

    (Department of Economics, Delhi School of Economics, Delhi, India)

  • Lokendra Kumawat

    (Department of Economics, Ramjas College,University of Delhi, Delhi)

Abstract

This paper models the seasonal dynamics in quarterly industrial production for India. For this, we extend the time-varying smooth transition autoregression (TV-STAR) model to allow for independent regime-switching behaviour in the deterministic seasonal and cyclical components. This yields the time-varying seasonal smooth transition (TV-SEASTAR) model. We find evidence of the effect of rainfall growth on seasonal dynamics of industrial production. We also find that the seasonal dynamics have changed over the past decade, one aspect of this being the significant narrowing down of seasonals. The timing of these changes coincides with the changes in the character of the economy as it progressed towards a free-market economy in the post liberalization period.

Suggested Citation

  • Pami Dua & Lokendra Kumawat, 2007. "Modelling Seasonal Dynamics in Indian Industrial Production--An Extention of TV-STAR Model," Working papers 162, Centre for Development Economics, Delhi School of Economics.
  • Handle: RePEc:cde:cdewps:162
    as

    Download full text from publisher

    File URL: http://www.cdedse.org/pdf/work162.pdf
    Download Restriction: no

    References listed on IDEAS

    as
    1. Matas-Mir, Antonio & Osborn, Denise R., 2004. "Does seasonality change over the business cycle? An investigation using monthly industrial production series," European Economic Review, Elsevier, vol. 48(6), pages 1309-1332, December.
    2. Cecchetti, Stephen G. & Kashyap, Anil K, 1996. "International cycles," European Economic Review, Elsevier, vol. 40(2), pages 331-360, February.
    3. Pami Dua & Lokendra Kumawat, 2005. "Modelling and Forecasting Seasonality in Indian Macroeconomic Time Series," Working papers 136, Centre for Development Economics, Delhi School of Economics.
    4. Terasvirta, T & Anderson, H M, 1992. "Characterizing Nonlinearities in Business Cycles Using Smooth Transition Autoregressive Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages 119-136, Suppl. De.
    5. Cecchetti, Stephen G & Kashyap, Anil K & Wilcox, David W, 1997. "Interactions between the Seasonal and Business Cycles in Production and Inventories," American Economic Review, American Economic Association, vol. 87(5), pages 884-892, December.
    6. Franses, Ph.H.B.F. & de Bruin, P. & van Dijk, D.J.C., 2000. "Seasonal smooth transition autoregression," Econometric Institute Research Papers EI 2000-06/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    7. Kanwar, Sunil, 2000. "Does the Dog Wag the Tail or the Tail the Dog? Cointegration of Indian Agriculture with Nonagriculture," Journal of Policy Modeling, Elsevier, vol. 22(5), pages 533-556, September.
    8. Beaulieu, J Joseph & Miron, Jeffrey A, 1992. "A Cross Country Comparison of Seasonal Cycles and Business Cycles," Economic Journal, Royal Economic Society, vol. 102(413), pages 772-788, July.
    9. Eitrheim, Oyvind & Terasvirta, Timo, 1996. "Testing the adequacy of smooth transition autoregressive models," Journal of Econometrics, Elsevier, vol. 74(1), pages 59-75, September.
    10. Pami Dua & Anirvan Banerji, 2012. "Business And Growth Rate Cycles In India," Working papers 210, Centre for Development Economics, Delhi School of Economics.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Seasonality; Smooth transition autoregression; Economic reforms.;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

    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:cde:cdewps:162. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sanjeev Sharma). General contact details of provider: http://edirc.repec.org/data/cdudein.html .

    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.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.