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A comparison of different forecasting models of the international trade in India

Author

Listed:
  • Aviral Kumar Tiwari

    (ICFAI University Tripura)

  • Claudiu T Albulescu

    (Politehnica University of Timisoara)

  • Phouphet Kyophilavong

    (National University of Laos)

Abstract

We assess the out-of-sample forecasting performance of eight models on Indian real exports and imports. The results, in large part show only a slight increase or no clear increase in India's international trade. However, the results are very robust when comparing the predictions in terms of exports and imports. In both the cases, the ARIMA and ETS models perform better in terms of the accuracy fit.

Suggested Citation

  • Aviral Kumar Tiwari & Claudiu T Albulescu & Phouphet Kyophilavong, 2014. "A comparison of different forecasting models of the international trade in India," Economics Bulletin, AccessEcon, vol. 34(1), pages 420-429.
    • Handle: RePEc:ebl:ecbull:eb-14-00017
    as

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    References listed on IDEAS

    as
    1. Alysha M De Livera & Rob J Hyndman, 2009. "Forecasting time series with complex seasonal patterns using exponential smoothing," Monash Econometrics and Business Statistics Working Papers 15/09, Monash University, Department of Econometrics and Business Statistics.
    2. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    3. Hyndman, Rob J. & Billah, Baki, 2003. "Unmasking the Theta method," International Journal of Forecasting, Elsevier, vol. 19(2), pages 287-290.
    4. Hyndman, Rob J. & Koehler, Anne B. & Snyder, Ralph D. & Grose, Simone, 2002. "A state space framework for automatic forecasting using exponential smoothing methods," International Journal of Forecasting, Elsevier, vol. 18(3), pages 439-454.
    5. Assimakopoulos, V. & Nikolopoulos, K., 2000. "The theta model: a decomposition approach to forecasting," International Journal of Forecasting, Elsevier, vol. 16(4), pages 521-530.
    6. Vipin Arora, 2013. "Comparisons of Chinese and Indian Energy Consumption Forecasting Models," Economics Bulletin, AccessEcon, vol. 33(3), pages 2110-2121.
    7. Gould, Phillip G. & Koehler, Anne B. & Ord, J. Keith & Snyder, Ralph D. & Hyndman, Rob J. & Vahid-Araghi, Farshid, 2008. "Forecasting time series with multiple seasonal patterns," European Journal of Operational Research, Elsevier, vol. 191(1), pages 207-222, November.
    8. Lin, Boqiang & Wang, Ting, 2012. "Forecasting natural gas supply in China: Production peak and import trends," Energy Policy, Elsevier, vol. 49(C), pages 225-233.
    9. Doornik Jurgen A & Ooms Marius, 2004. "Inference and Forecasting for ARFIMA Models With an Application to US and UK Inflation," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 8(2), pages 1-25, May.
    10. J W Taylor, 2003. "Short-term electricity demand forecasting using double seasonal exponential smoothing," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(8), pages 799-805, August.
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    More about this item

    Keywords

    Trade; Forecasting; India; Exponential smoothing; Holt–Winters; Box–Jenkins;
    All these keywords.

    JEL classification:

    • F1 - International Economics - - Trade
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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