IDEAS home Printed from https://ideas.repec.org/p/cns/cnscwp/200303.html
   My bibliography  Save this paper

Quantitative forecasting for Tourisme: OLS and ARIMAX approaches

Author

Listed:
  • M. Pulina

Abstract

The paper analyses, estimates and forecasts the demand for international and domestic tourism to Sardinia (Italy). Monthly data are used for the sample period from 1987 to 2002. Concepts such as seasonal and long run unit roots are employed. Two econometric approaches, the OLS and ARIMAX, are used that give satisfactory results in terms of both the estimation and forecasting phases. A full range of diagnostic tests is provided. An ex-ante forecasting exercise is run for tourism demand to Sardinia for the period between January and December 2003.

Suggested Citation

  • M. Pulina, 2003. "Quantitative forecasting for Tourisme: OLS and ARIMAX approaches," Working Paper CRENoS 200303, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    • Handle: RePEc:cns:cnscwp:200303
    as

    Download full text from publisher

    File URL: https://crenos.unica.it/crenos/node/189
    Download Restriction: no
    File URL: https://crenos.unica.it/crenos/sites/default/files/wp/03-03.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Franses, Philip Hans, 1991. "Seasonality, non-stationarity and the forecasting of monthly time series," International Journal of Forecasting, Elsevier, vol. 7(2), pages 199-208, August.
    2. Haiyan Song & Peter Romilly & Xiaming Liu, 2000. "An empirical study of outbound tourism demand in the UK," Applied Economics, Taylor & Francis Journals, vol. 32(5), pages 611-624.
    3. Findley, David F, et al, 1998. "New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 127-152, April.
    4. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    5. Pulina, M. & O'Brien, R.J., 2002. "Monthly, annual and quarterly frequencies: a comparison of models for tourism in Sardinia and bounded rationality," Discussion Paper Series In Economics And Econometrics 206, Economics Division, School of Social Sciences, University of Southampton.
    6. Findley, David F, et al, 1998. "New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 169-177, April.
    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. McElroy, Tucker & Politis, Dimitris N., 2013. "Distribution theory for the studentized mean for long, short, and negative memory time series," Journal of Econometrics, Elsevier, vol. 177(1), pages 60-74.
    2. Pulina, M. & O'Brien, R.J., 2002. "Monthly, annual and quarterly frequencies: a comparison of models for tourism in Sardinia and bounded rationality," Discussion Paper Series In Economics And Econometrics 0206, Economics Division, School of Social Sciences, University of Southampton.
    3. Chrigui Zouhair & Boujelbene Younes, 2009. "The Opportunities for Adopting Inflation Targeting in Tunisia: a Cointegration Study and Transmission Channels of Monetary Policy," Transition Studies Review, Springer;Central Eastern European University Network (CEEUN), vol. 16(3), pages 671-692, October.
    4. Cruz-Rodríguez, Alexis, 2004. "Un análisis del ciclo económico de la República Dominicana bajo cambios de régimen [Analysis of business cycle of the Dominican Republic using Markov Switching model]," MPRA Paper 54352, University Library of Munich, Germany.
    5. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    6. Giancarlo Bruno & Edoardo Otranto, 2006. "The choice of time interval in seasonal adjustment: A heuristic approach," Statistical Papers, Springer, vol. 47(3), pages 393-417, June.
    7. Mauricio Gallardo & Hernán Rubio, 2009. "Diagnóstico de estacionalidad con X-12-ARIMA," Economic Statistics Series 76, Central Bank of Chile.
    8. Hall, Viv B & Thomson, Peter, 2022. "A boosted HP filter for business cycle analysis: evidence from New Zealand’s small open economy," Working Paper Series 9473, Victoria University of Wellington, School of Economics and Finance.
    9. Kroes, James R. & Manikas, Andrew S. & Gattiker, Thomas F., 2018. "Operational leanness and retail firm performance since 1980," International Journal of Production Economics, Elsevier, vol. 197(C), pages 262-274.
    10. Quenneville, Benoit & Ladiray, Dominique & Lefrancois, Bernard, 2003. "A note on Musgrave asymmetrical trend-cycle filters," International Journal of Forecasting, Elsevier, vol. 19(4), pages 727-734.
    11. Massmann, Michael & Mitchell, James, 2003. "Reconsidering the evidence: Are Eurozone business cycles converging," ZEI Working Papers B 05-2003, University of Bonn, ZEI - Center for European Integration Studies.
    12. Allison Zhou & Carl Bonham & Byron Gangnes, 2007. "Modeling the supply and demand for tourism: a fully identified VECM approach," Working Papers 200717, University of Hawaii at Manoa, Department of Economics.
    13. Hai Yue Liu & Xiao Lan Chen, 2017. "The imported price, inflation and exchange rate pass-through in China," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1279814-127, January.
    14. Henryk Gurgul & Marcin Suder, 2013. "The Properties of ATMs Development Stages - an Empirical Analysis," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 14(3), pages 443-466, September.
    15. Carlos A. Medel, 2018. "A Comparison Between Direct and Indirect Seasonal Adjustment of the Chilean GDP 1986–2009 with X-12-ARIMA," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 14(1), pages 47-87, April.
    16. Stefania D'Amico & Athanasios Orphanides, 2008. "Uncertainty and disagreement in economic forecasting," Finance and Economics Discussion Series 2008-56, Board of Governors of the Federal Reserve System (U.S.).
    17. Kirchner, Robert, 1999. "Auswirkungen des neuen Saisonbereinigungsverfahrens Census X-12-ARIMA auf die aktuelle Wirtschaftsanalyse in Deutschland," Discussion Paper Series 1: Economic Studies 1999,07, Deutsche Bundesbank.
    18. repec:wyi:journl:002126 is not listed on IDEAS
    19. M. Angeles Carnero & Siem Jan Koopman & Marius Ooms, 2003. "Periodic Heteroskedastic RegARFIMA Models for Daily Electricity Spot Prices," Tinbergen Institute Discussion Papers 03-071/4, Tinbergen Institute.
    20. Flávio de Freitas Val & Wagner Piazza Gaglianone & Marcelo Cabus Klotzle & Antonio Carlos Figueiredo Pinto, 2017. "Estimating the Credibility of Brazilian Monetary Policy using Forward Measures and a State-Space Model," Working Papers Series 463, Central Bank of Brazil, Research Department.
    21. Viv B. Hall & Peter Thomson, 2021. "Does Hamilton’s OLS Regression Provide a “better alternative” to the Hodrick-Prescott Filter? A New Zealand Business Cycle Perspective," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 17(2), pages 151-183, November.

    More about this item

    Keywords

    monthly data; unit roots; ols; arimax;
    All these keywords.

    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:cns:cnscwp:200303. 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: CRENoS (email available below). General contact details of provider: https://edirc.repec.org/data/crenoit.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.