Agric. Econ. - Czech, 2015, 61(9):422-428 | DOI: 10.17221/209/2014-AGRICECON

Impact of price realization on India's tea export: Evidence from Quantile Autoregressive Distributed Lag ModelOriginal Paper

Debdatta PAL, Subrata Kumar MITRA
Indian Institute of Management Raipur, Raipur, India

The quantile autoregressive distributed lag model of Galvao et al. (2013) was employed to assess the impact of price realization on India's tea export. The results of the QADL varied significantly from the conditional mean estimates. It was found that the tea export from India had autoregressive impact, and that production and export price realization had asymmetric relationship with India's tea export that varied over quantiles.

Keywords: export price realization, Quantile Autoregressive Distributed Lag model, India

Published: September 30, 2015  Show citation

ACS AIP APA ASA Harvard Chicago Chicago Notes IEEE ISO690 MLA NLM Turabian Vancouver
PAL D, MITRA SK. Impact of price realization on India's tea export: Evidence from Quantile Autoregressive Distributed Lag Model. Agric. Econ. - Czech. 2015;61(9):422-428. doi: 10.17221/209/2014-AGRICECON.
Share...
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. Barsky R., Delong B. (1993): Why does the stock market fluctuate? Quarterly Journal of Economics, 108: 291-311. Go to original source...
    2. Chatterjee S. (2011): Domestic Production, Domestic Consumption and Exports of Indian Tea: Examining the Interlinkages. In: Procceding 7th Annual Conference on Economic Growth and Development, Indian Statistical Institute, New Delhi, Dec 15-17, 2011.
    3. Dickey D.A., Fuller W.A. (1979): Distribution of the estimators for autoregressive time series with a unit root, Journal of American Statistical Association, 74: 427-431. Go to original source...
    4. Donaldson R., Kamstra M. (1996): A new dividend forecasting procedure that rejects bubbles in asset prices: The case of 1929's stock crash. Review of Financial Studies, 9: 333-383. Go to original source...
    5. Galvao A.F., Montes-Rojas G., Park S.Y. (2009): Quantile Autoregressive Distributed Lag Model with an Application to House Price Returns. Report No. 09/04, Department of Economics, City University London, London.
    6. Galvao Jr, A.F., Montes-Rojas G., Park S.Y. (2013): Quantile autoregressive distributed lag model with an application to house price returns. Oxford Bulletin of Economics and Statistics, 75: 307-321. Go to original source...
    7. Johansen S. (1991): Estimating and testing cointegration vectors in Gaussian vector autoregressive models. Econometrica, 59: 1551-1580. Go to original source...
    8. Koenker R., Xiao Z. (2006): Quantile autoregression. Journal of the American Statistical Association, 101: 980-990. Go to original source...
    9. Kwiatkowski D., Phillips P.C.B., Schmidt P., Shin Y. (1992): Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics, 54: 159-178. Go to original source...
    10. Lee C., Zee J. (2011): Revisiting the relationship between spot and futures oil prices: Evidence from quantile cointegrating regression. Energy Economics, 33: 924-935. Go to original source...
    11. MacKinnon J.G., Haug A.A., Michelis L. (1999): Numerical distribution functions of likelihood ratio tests for cointegration. Journal of Applied Econometrics, 14: 563-577. Go to original source...
    12. ONICRA (2014): The Indian Tea Industry. Available at http://www.onicra.com/images/pdf/publications/teaindustryrepor3.03.2014.pdf
    13. Phillips P.C.B., Perron P. (1988): Testing for a unit root in time series regression. Biometrika, 75: 335-346. Go to original source...
    14. Xiao Z. (2009): Quantile cointegrating regression. Journal of Econometrics, 150: 248-260. Go to original source...

    This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY NC 4.0), which permits non-comercial use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content