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Information Journal Paper

Title

SEASONAL FORECASTING OF AGRICULTURE GROSS DOMESTIC PRODUCTION IN IRAN: APPLICATION OF PERIODIC AUTOREGRESSIVE MODEL

Pages

  35-44

Abstract

 Agriculture as one of the major economic sectors of Iran, has an important role in Gross Domestic Production by providing about 14% of GDP. This study attempts to forecast the value of the agriculture GDP using PERIODIC AUTOREGRESSIVE MODEL (PAR), as the new seasonal time series techniques. To address this aim, the quarterly data were collected from March 1988 to July 1989. The collected data was firstly analyzed using PERIODIC UNIT ROOT TEST Franses & Paap (2004). The analysis found non-periodic unit root in the seasonal data. Second, periodic seasonal behavior (Boswijk & Franses, 1996) was examined. The results showed that PERIODIC AUTOREGRESSIVE MODEL fits agriculture GDP well. This makes an accurate forecast of agriculture GDP possible. Using the estimated model, the future value of quarter agricultural GDP from March 2011 to July 2012 was forecasted. With consideration to the fair fit of this model with agricultural GDP, It is recommended to use PERIODIC AUTOREGRESSIVE MODEL for the future studies.

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    APA: Copy

    GHAHREMANZADEH, M., & ALEFI, KH.. (2014). SEASONAL FORECASTING OF AGRICULTURE GROSS DOMESTIC PRODUCTION IN IRAN: APPLICATION OF PERIODIC AUTOREGRESSIVE MODEL. JOURNAL OF ECONOMICS AND AGRICULTURE DEVELOPMENT, 28(1), 35-44. SID. https://sid.ir/paper/142104/en

    Vancouver: Copy

    GHAHREMANZADEH M., ALEFI KH.. SEASONAL FORECASTING OF AGRICULTURE GROSS DOMESTIC PRODUCTION IN IRAN: APPLICATION OF PERIODIC AUTOREGRESSIVE MODEL. JOURNAL OF ECONOMICS AND AGRICULTURE DEVELOPMENT[Internet]. 2014;28(1):35-44. Available from: https://sid.ir/paper/142104/en

    IEEE: Copy

    M. GHAHREMANZADEH, and KH. ALEFI, “SEASONAL FORECASTING OF AGRICULTURE GROSS DOMESTIC PRODUCTION IN IRAN: APPLICATION OF PERIODIC AUTOREGRESSIVE MODEL,” JOURNAL OF ECONOMICS AND AGRICULTURE DEVELOPMENT, vol. 28, no. 1, pp. 35–44, 2014, [Online]. Available: https://sid.ir/paper/142104/en

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