explanation of structural changes in the modeling of Iran's tourism demand
Subject Areas :
Javad Barati
1
1 - Professor in Tourism Economics Department, Tourism Research Institute, Academic staff of Iranian Academic Center for Education Culture & Research (ACECR), Mashhad, Iran,
Received: 2022-08-27
Accepted : 2022-09-18
Published : 2022-08-23
Keywords:
Tourism Industry,
Foreign Tourism,
generalized fluctuation,
R software,
: structural breaks,
Abstract :
The expansion of statistics and the possibility of using time series information in tourism economics studies have led to the increasing use of time series models in research of tourism economics; also, due to the high impact of various factors such as disease, war, sanctions, political events, economic shocks and many other international and national shocks on the tourism industry, structural changes occur in this industry which make the common results in time series surveys unreliable. To solve this problem, structural break points in tourism industry studies should be identified to be the basis for researchers' modeling in strategic studies and policy-making. The present study aimed to determine the structural break points in the Iranian tourism industry, using quarterly data from spring 2013 to summer 1399. All these tests have confirmed the existence of a structural break in the fall of 2019. However, if the coefficient decreases significantly, only the fall of 2019 would be recognized as a breakpoint in the Iranian economy. Due to the high volume of tourism and the growth of this sector in the spring and summer of 2019, it is expected that in the annual surveys, 2020 will be the only year that a structural break has occurred in tourism in Iran. Political events such as the Saudi Embassy or the referendum in Iraq Kurdistan, the signing of the UN Security Council, or the imposition of severe sanctions on Iran's economy have virtually failed to bring about structural changes in Iran's time series tourism models. There are more pervasive factors such as the Covid-19 epidemic that have caused a structural break in Iranian tourism.
References:
برکچیان، سیدمهدی؛ بیات، سعید و کرمی، هومن (1394). شکستهای ساختاری و مدلسازی رفتار تورم: مقایسه مدلهای غیرخطی و مدلهای با پارامتر زمانمتغیر. مجله مطالعات و سیاستهای اقتصادی، سال 11، شماره 1، 51-74.
فرزین، محمدرضا؛ زندی، ابتهال؛ عبدی، مرجان و عباسپور، نیلوفر (1394). شناسایی بخشهای کلیدی در صنعت گردشگری ایران (بر مبنای مدل داده-ستانده). فصلنامه اقتصاد مالی؛ دوره 9، شماره 32، صفحات 65-81.
Andrews, D. W. K. (1993). Tests for Parameter Instability and Structural Change with Unknown Change Point. Econometrica, 61, 821–856.
Andrews, D. W. K. and Ploberger, W. (1994). Optimal Tests When a Nuisance Parameter is Present Only under the Alternative. Econometrica, 62, 1383–1414.
Baggio, R., and R. Ruggero (2011). Complex and Chaotic Tourism Systems: Towards a Quantitative Approach. Journal of Contemporary Hospitality Management 23:840-861.
Bai, J. (1997). Estimation of a Change Point in Multiple Regression Models. Review of Economics and Statistics, 79, 551–563.
Bai, J. and Perron, P. (1998). Computation and Analysis of Multiple Structural Change Models; Forthcoming.
Bai, J. and Perron, P. (2002). Computation and analysis of multiple structural change models; Journal of Applied Econometrics, 18 (2002), pp. 1-22.
Bai, J., & Perron, P. (2005). Multiple structural change models: a simulation analysis. In Econometric Theory and Practice: Frontiers of Analysis and Applied Research, D. Corbea, S. Durlauf and B.E. Hansen (eds.), Cambridge University Press, 212-237.
Bento, João Paulo Cerdeira (2016). Tourism and economic growth in Portugal: an empirical investigation of causal links. Tourism & Management Studies, 12(1), 164-171
Brown, R. L., Durbin, J., and Evans, J. M. (1975). Techniques for Testing the Constancy of Regression Relationships over Time. Journal of the Royal Statistical Society, B 37, 149–163.
Chow, G. (1960). Tests of equality between sets of coefficients in two linear regression. Econometrica, 28: 591–605.
Chowdhury, Khorshed (2018). Dynamic and Structural Breaks in Tourist Arrival in Australia. CIU Journal, 1(1). 1-19.
Chu, C.-S. J., Hornik, K., and Kuan, C.-M. (1995). The Moving-Estimates Test for Parameter Stability. Econometric Theory, 11, 669–720.
Çoban, Berhan & Firuzan, Esin (2019). Convergence and Cointegration Analysis under Structural Breaks: Application of Turkey Tourism Markets. Sosyoekonomi, Vol. 27(39), 95-110.
Eksi, O. (2009). Structural break estimation: a survey working paper, Universitat Pompeu Fabra, 9 p.
Gałecki, Maciej (2020). Testing for structural breaks in tourist movements in the European Union. Journal of Physical Education and Sport, Vol 20 (Supplement issue 5), Art 377 pp 2770 – 2777.
Gouveia, Pedro MDCB (2014). A note on seasonal breaks in tourism demand time series. Tourism and Hospitality Research, Vol. 14(3) 123–130.
Hawkins, D.M., (2001). Fitting multiple change-point models to data. Comput. Statist. Data Anal; 37, 323–341.
Hornik, Kurt; Leisch, Friedrich; Kleiber, Christian & Zeileis, Achim (2005). Monitoring structural change in dynamic econometric models; Journal of Applied Econometrics, 2005, vol. 20, issue 1, 99-121.
Johnston, Nicholas E. & Aday, James Brian (2015). Chaos theory, tourism. Encyclopedia of Tourism
Karagoz, M. (2008). The effect of terrorism on tourism: evidence from Turkey. Conference Proceedings, pp. 132-144, The EUTO Conference, Christel DeHaan Tourism and Travel Research Institute, The University of Nottingham.
Kim, I-M and Maddala, G.S. (1991). Multiple structural breaks and unit roots in exchange rates. Mimeo, University of Florida.
Kim, Soo-Eun & Seok, Jun Ho. (2018). Tourism and Economic Growth in Korea: Focusing on the Structural Break. Journal of International Trade & Commerce, Vol.14, No.4, pp.177-188
Kuan, C.-M. & Hornik, K. (1995). The Generalized Fluctuation Test: A Unifying View. Econometric Reviews, 14, 135–161.
Leiper, N. (1990). Tourism systems. An interdisciplinary perspective. Occasional Paper No. 2, Department of Management Systems, Business Studies Faculty, Massey University, Palmerston North, New Zealand.
Liu, J., Wu, S., & Zidek, J. V. (1997). On segmented multivariate regressions. Statistica Sinica, 7, 497-525.
Lorenz, E. (1972). Predictability: does the flap of a butterfly’s wing in Brazil set off a tornado in Texas? Na.
Maasoumi, Esfandiar; Zaman, Asad; Ahmed, Mumtaz (2010). Tests for structural change, aggregation, and homogeneity. Economic Modelling. 27 (6): 1382–1391.
McLennan, Char-lee, Ruhanen, Lisa, Ritchie, Brent, Pham, Tien (2012). Dynamics of Destination Development Investigating the Application of Transformation Theory. Journal of Hospitality & Tourism Research, 36(2), 164-190.
Merriam‐Webster, Inc. (1990). Webster’s ninth new collegiate dictionary. Springfield, MA: Merriam‐Webster Publishers.
Min, Jennifer C. H.; Kung, Hsien-Hung & Chang, Tsangyao (2019). Testing the Structural Break of Taiwan Inbound Tourism Markets. Journal for Economic Forecasting, issue 2, 117-130.
Muthuramu, p. & Maheswari, T. Uma (2019). Tests for Structural Breaks in Time Series Analysis: A Review of Recent Development. Shanlax International Journal of Economics, vol. 7, no. 4, pp. 66–79.
Ploberger, W., Kr¨amer, W., and Kontrus, K. (1989). A New Test for Structural Stability in the Linear Regression Model. Journal of Econometrics, 40, 307–318.
Perron, P. (1989). The great crash, the oil price shock, and the unit root hypothesis. Econometrica, 57, 4: 1361–1401.
Quandt, R. (1960). Tests of the hypothesis that a linear Regression obeys two separate regimes. Journal of the American Statistical Association, 55: 324–30.
Russell, R., and B. Faulkner (2004). Entrepreneurship, Chaos and the Tourism Area Lifecycle. Annals of Tourism Research 31:556-579.
Salazar, Diego. (1982). Structural Changes in Time Series Models. Journal of Econometrics, 19, 147-163.
Stock, J. H., Watson, M. W (2003), Introduction to econometrics. Boston: Addison Wesley. 696 p. ISBN 0-321-22351-9.
Sullivan, J.H., (2002). Estimating the locations of multiple change points in the mean. Comput. Statist; 17, 289–296.
Tukey JW (1962). The Future of Data Analysis. Annals of Mathematical Statistics, 33, 1–67.
Zeileis, Achim. (2005). A unified approach to structural change tests based on ML scores, F statistics, and OLS residuals. Econometric Reviews; 24(4):445–466.
Zeileis, Achim; Leisch. F.; Hornik, Kurt & Kleiber, Christian (2002). Strucchange: An R package for testing for structural change in linear regression models; Technical Reports from Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen
Zeileis, Achim; Kleiber, Christian; Kramer, Walter & Hornik, Kurt (2003). Testing and Dating of Structural Changes in Practice. Computational Statistics and Data Analysis, 44, 109-123.
_||_