IDEAS home Printed from https://ideas.repec.org/a/bnu/journl/v10y2018i4p96-105.html
   My bibliography  Save this article

Forecast scenarios for the development of agricultural land market

Author

Listed:
  • Stupen R.M.

    (Lviv National Agrarian University)

Abstract

forecasting the development of the agricultural land market. The development trends of the main indicators of the domestic market for agricultural lands are analyzed. It was determined that in modern conditions the agricultural land market is still at the stage of formation, which is primarily associated with the free transfer of land to citizens and their depreciation in the privatization process, as well as the extension of the moratorium on the sale of land in this category. Given the likely economic changes and possible options for the transformation of land relations, three main forecast scenarios for the development of the agricultural land market in Ukraine for the future are justified: realistic (inertial), optimistic and pessimistic. For each of these scenarios, the main directions of development in the context of the areas of transformation of the agricultural land market, in particular the introduction of free circulation of land, infrastructure, financial and information support, etc., are substantiated. Based on the Holt’s method of exponential smoothing, a forecast was made of the land plots with which transactions took place on the market turnover elements as of 2020, depending on the development scenarios of this market. A predictive model of indicators of land plots with which transactions occurred, which provides for their modeling based on multiple regression, is proposed. The evaluation of the adequacy of this model according to the Fisher criterion was carried out. The use of the proposed model will allow to form reliable perspective indicators of the development of the agricultural land market, in particular the areas of land plots with which the transactions took place, depending on the influence factors and scenarios of its development.

Suggested Citation

  • Stupen R.M., 2018. "Forecast scenarios for the development of agricultural land market," Balanced Nature Using, Institute of agroecology and environmental management, vol. 10(4), pages 96-105, December.
  • Handle: RePEc:bnu:journl:v:10:y:2018:i:4:p:96-105
    as

    Download full text from publisher

    File URL: http://natureus.org.ua/repec/archive/4_2018/12.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Holt, Charles C., 2004. "Author's retrospective on 'Forecasting seasonals and trends by exponentially weighted moving averages'," International Journal of Forecasting, Elsevier, vol. 20(1), pages 11-13.
    2. Holt, Charles C., 2004. "Forecasting seasonals and trends by exponentially weighted moving averages," International Journal of Forecasting, Elsevier, vol. 20(1), pages 5-10.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yuxin Zhang & Yifei Yang & Xiaosi Li & Zijing Yuan & Yuki Todo & Haichuan Yang, 2023. "A Dendritic Neuron Model Optimized by Meta-Heuristics with a Power-Law-Distributed Population Interaction Network for Financial Time-Series Forecasting," Mathematics, MDPI, vol. 11(5), pages 1-20, March.
    2. Simona Mikšíková & David Ulčák & František Kuda, 2022. "Analysis of Malfunctions in Selected Parking Systems in the Czech Republic," Sustainability, MDPI, vol. 14(3), pages 1-10, February.
    3. Liu, Che & Sun, Bo & Zhang, Chenghui & Li, Fan, 2020. "A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine," Applied Energy, Elsevier, vol. 275(C).
    4. Hossein Yousefi & Mohammad Hasan Ghodusinejad & Armin Ghodrati, 2022. "Multi-Criteria Future Energy System Planning and Analysis for Hot Arid Areas of Iran," Energies, MDPI, vol. 15(24), pages 1-25, December.
    5. Dyna Heng & Anna Ivanova & Rodrigo Mariscal & Ms. Uma Ramakrishnan & Joyce Wong, 2016. "Advancing Financial Development in Latin America and the Caribbean," IMF Working Papers 2016/081, International Monetary Fund.
    6. Kang, Wensheng & Ratti, Ronald A. & Vespignani, Joaquin L., 2016. "The implications of monetary expansion in China for the US dollar," Journal of Asian Economics, Elsevier, vol. 46(C), pages 71-84.
    7. Kim, Yochan & Park, Jinkyun & Jung, Wondea, 2017. "A quantitative measure of fitness for duty and work processes for human reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 595-601.
    8. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2022. "Forecasting natural gas consumption using Bagging and modified regularization techniques," Energy Economics, Elsevier, vol. 106(C).
    9. Guo-hua Ye & Mirxat Alim & Peng Guan & De-sheng Huang & Bao-sen Zhou & Wei Wu, 2021. "Improving the precision of modeling the incidence of hemorrhagic fever with renal syndrome in mainland China with an ensemble machine learning approach," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-13, March.
    10. Ahmed Belhadjayed & Grégoire Loeper & Frédéric Abergel, 2016. "Forecasting Trends With Asset Prices," Post-Print hal-01512431, HAL.
    11. Karzan Mahdi Ghafour & Abdulqadir Rahomee Ahmed Aljanabi, 2023. "The role of forecasting in preventing supply chain disruptions during the COVID-19 pandemic: a distributor-retailer perspective," Operations Management Research, Springer, vol. 16(2), pages 780-793, June.
    12. Fieger, Peter & Rice, John, 2016. "Modelling Chinese Inbound Tourism Arrivals into Christchurch," MPRA Paper 75468, University Library of Munich, Germany.
    13. Koopman, Siem Jan & Ooms, Marius, 2006. "Forecasting daily time series using periodic unobserved components time series models," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 885-903, November.
    14. Albrecht, Tobias & Rausch, Theresa Maria & Derra, Nicholas Daniel, 2021. "Call me maybe: Methods and practical implementation of artificial intelligence in call center arrivals’ forecasting," Journal of Business Research, Elsevier, vol. 123(C), pages 267-278.
    15. Sprangers, Olivier & Schelter, Sebastian & de Rijke, Maarten, 2023. "Parameter-efficient deep probabilistic forecasting," International Journal of Forecasting, Elsevier, vol. 39(1), pages 332-345.
    16. Kosuke Kawakami & Hirokazu Kobayashi & Kazuhide Nakata, 2021. "Seasonal Inventory Management Model for Raw Materials in Steel Industry," Interfaces, INFORMS, vol. 51(4), pages 312-324, July.
    17. Hu, Yuntong & Xiao, Fuyuan, 2022. "A novel method for forecasting time series based on directed visibility graph and improved random walk," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
    18. Xianbo Li, 2022. "Sequence Model and Prediction for Sustainable Enrollments in Chinese Universities," Sustainability, MDPI, vol. 15(1), pages 1-25, December.
    19. Andrea Kolková & Petr Rozehnal, 2022. "Hybrid demand forecasting models: pre-pandemic and pandemic use studies," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 17(3), pages 699-725, September.
    20. Feng Xu & Mohamad Sepehri & Jian Hua & Sergey Ivanov & Julius N. Anyu, 2018. "Time-Series Forecasting Models for Gasoline Prices in China," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(12), pages 1-43, December.

    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:bnu:journl:v:10:y:2018:i:4:p:96-105. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Olexii Shkuratov (email available below). General contact details of provider: https://edirc.repec.org/data/iaeemua.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.