IDEAS home Printed from https://ideas.repec.org/p/pre/wpaper/201363.html
   My bibliography  Save this paper

Time-Varying Linkages between Tourism Receipts and Economic Growth in South Africa

Author

Listed:
  • Mehmet Balcilar

    () (Department of Economics, Eastern Mediterranean University, Famagusta, North Cyprus,via Mersin 10, Turkey)

  • Renee van Eyden

    () (Department of Economics, University of Pretoria)

  • Roula Inglesi-Lotz

    () (Department of Economics, University of Pretoria)

  • Rangan Gupta

    () (Department of Economics, University of Pretoria)

Abstract

The causal link between tourism receipts and GDP has recently become a major focus in the tourism economics literature. Results obtained in recent studies about the causal link appear to be sensitive with respect to the countries analysed, sample period and methodology employed. Considering the sensitivity of the causal link, we use the rolling window and time-varying coefficient estimation methods to analyse the parameter stability and Granger causality based on a vector error correction model (VECM). When applied to South Africa for the 1960-2011 periods, the findings are as follows: results from the full sample VECM indicate that there is no Granger-causality between the tourism receipts and GDP, while the findings from the time-varying coefficients model based on the state-space representation and rolling window estimation technique show that GDP has no predictive power for tourism receipts; however, tourism receipts have positive-predictive content for GDP for the entire period, with the exception of the period between 1985 and 1990.

Suggested Citation

  • Mehmet Balcilar & Renee van Eyden & Roula Inglesi-Lotz & Rangan Gupta, 2013. "Time-Varying Linkages between Tourism Receipts and Economic Growth in South Africa," Working Papers 201363, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:201363
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hassani, Hossein & Silva, Emmanuel Sirimal & Antonakakis, Nikolaos & Filis, George & Gupta, Rangan, 2017. "Forecasting accuracy evaluation of tourist arrivals," Annals of Tourism Research, Elsevier, vol. 63(C), pages 112-127.
    2. De Bruyn C & Meyer N & Meyer D.F, 2018. "Assessing the Dynamic Economic Impact of Tourism in a Developing Region in South Africa," Journal of Economics and Behavioral Studies, AMH International, vol. 10(5), pages 274-283.
    3. Tsangyao Chang & Hsiao-Ping Chu & Frederick W. Deale & Rangan Gupta & Stephen M. Miller, 2017. "The relationship between population growth and standard-of-living growth over 1870–2013: evidence from a bootstrapped panel Granger causality test," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 44(1), pages 175-201, February.
    4. Shahbaz, Muhammad & Ferrer, Román & Hussain Shahzad, Syed Jawad & Haouas, Ilham, 2017. "Is the tourism-economic growth nexus time-varying? Bootstrap rolling-window causality analysis for the top ten tourist destinations," MPRA Paper 82713, University Library of Munich, Germany, revised 04 Nov 2017.
    5. Muhammad Akram GILAL* & Muhammad AJMAIR** & Sohail FAROOQ***, 2019. "Structural Changes And Economic Growth In Pakistan," Pakistan Journal of Applied Economics, Applied Economics Research Centre, vol. 29(1), pages 35-51.
    6. Muhammad Shahbaz & Román Ferrer & Syed Jawad Hussain Shahzad & Ilham Haouas, 2018. "Is the tourism–economic growth nexus time-varying? Bootstrap rolling-window causality analysis for the top 10 tourist destinations," Applied Economics, Taylor & Francis Journals, vol. 50(24), pages 2677-2697, May.
    7. Abdulnasser Hatemi-J & Rangan Gupta & Axel Kasongo & Thabo Mboweni & Ndivhuho Netshitenzhe, 2018. "Does tourism cause growth asymmetrically in a panel of G-7 countries? A short note," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 45(1), pages 49-57, February.
    8. Andrew Phiri, 2016. "Tourism and Economic Growth in South Africa: Evidence from Linear and Nonlinear Cointegration Frameworks," Managing Global Transitions, University of Primorska, Faculty of Management Koper, vol. 14(1 (Spring), pages 31-53.
    9. Buthaina M. A. Muhtaseb & Hussam-Eldin Daoud, 2017. "Tourism and Economic Growth in Jordan: Evidence from Linear and Nonlinear Frameworks," International Journal of Economics and Financial Issues, Econjournals, vol. 7(1), pages 214-223.
    10. Nino Fonseca & Marcelino Sánchez-Rivero, 2020. "Significance bias in the tourism-led growth literature," Tourism Economics, , vol. 26(1), pages 137-154, February.
    11. Shahzad, Syed Jawad Hussain & Shahbaz, Muhammad & Ferrer, Román & Kumar, Ronald Ravinesh, 2017. "Tourism-led growth hypothesis in the top ten tourist destinations: New evidence using the quantile-on-quantile approach," Tourism Management, Elsevier, vol. 60(C), pages 223-232.
    12. Mustafa Özer & Inci Oya Coşkun & Mustafa Kırca, 2015. "Time Varying Causality Between Exchange Rates And Tourism Demand For Turkey," Tourism Research Institute, Journal of Tourism Research, vol. 10(1), pages 125-142, June.

    More about this item

    Keywords

    Tourism receipts; economic growth; time-varying causality; time-varying parameter model;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism
    • O40 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - General

    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:pre:wpaper:201363. 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: (Rangan Gupta). General contact details of provider: http://edirc.repec.org/data/decupza.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.