A REWIEV OF TOURISM DEMAND ON TURKEY VIA SEEMINGLY UNRELATED REGRESSION MODELS

Year 2015, , 235 - 256, 20.04.2015

Mina Aksakal Çiğdem Arıcıgil Çilan

https://doi.org/10.18092/ijeas.84319

Cited By: 4

Abstract

Since the tourism sector is highly sensible to the factors affecting touristic product and services, it gives a significant importance to demand forecasting and analyses of the factors that affect the demand. This study aims to model the tourism demand of European countries, which have an important share of the Turkish tourism market. For this study, Seemingly Unrelated Regression (SUR) Model was used and the forecasting parameters were compared with Ordinary Least Square (OLS) outcomes. Addition to that, factors affecting European Countries’ tourism demand was analyzed and these countries were compared according to these factors. As a result of this study, parameter estimates of SUR model are more efficient than classical regression model parameter estimates. This study recommends SUR model, which is not widely used around the world and doesn’t take part in studies in Turkey to determine the tourism politics and tourism projection works, can be used as a decision method

Keywords

Seemingly Unrelated Regression, Tourism Demand, Ordinary Least Squares, Generalized

TÜRKİYE’YE YÖNELİK TURİZM TALEBİNİN GÖRÜNÜRDE İLİŞKİSİZ REGRESYON MODELLERİ İLE İNCELENMESİ

Abstract

Günümüzde turizm sektörünün turistik mal ve hizmetlerin talebine etki eden faktörlere karşı aşırı duyarlı olması bu sektördeki talep tahminlerini ve talebe etki eden faktörlerin analizini oldukça önemli duruma getirmektedir. Bu çalışmada Türkiye’nin Turizm alanındaki pazar payının önemli bir bölümünü oluşturan Avrupa ülkelerinin Türkiye’ye yönelik turizm talebinin modellenmesi amaçlanmıştır. Bu amaçla Görü-nürde İlişkisiz Regresyon Modelleri (GİR) kullanılmış ve bu modeller temel alınarak tahmin edilen para-metreler, En Küçük Kareler (EKK) tahminleri ile karşılaştırılmıştır. Ayrıca çalışma kapsamında seçilen Avrupa ülkelerinin Türkiye’ye yönelik turizm talebini etkileyen faktörler incelenmiş ve bu faktörlere göre ülkeler karşılaştırılmıştır. GİR modeli kullanılarak elde edilen parametre tahminlerinin klasik regresyon modeli tahmin tekniklerine kıyasla daha etkin sonuçlar verdiği gözlenmiştir. Bu çalışma, turizm planla-malarının yapılması ve turizm politikalarının belirlenmesinde dünyada yaygın olarak kullanılmayan, Tür-kiye’deki turizm talep tahmin ve modelleme çalışmalarında yer almamış GİR modelinin bir karar yöntemi olarak kullanılmasını önermektedir.

Keywords

Görünürde İlişkisiz Regresyon Modelleri, Turizm Talebi Tahmini, En Küçük Kare

References

• BAHAR, Ozan ve KOZAK, Metin (2008), Turizm Ekonomisi, İkinci Baskı, Ankara: Detay Yayıncılık
• BALDIGARA, Tea (2013), “Forecasting Tourism Demand in Croatia: A Comparison of Different Extrapolative Methods”, Journal of Business Administration Research, 2(1), 84-92
• BALTAGI, Badi, GARVIN, Susan ve KERMAN, Stephen (1989), “Further Monte Carlo Evidence on Seemingly Unrelated Regressions with Unequal Number of Observations,” Annales D′Economie et de Statistique, 14, 103-115.
• BALTAGI, Badi H. (2008), Econometrics, Dördüncü Baskı, Verlag Berlin Heidelberg: Springer
• BINKLEY, James K. ve NELSON Carl H. (1988), “A Note on the Efficiency of Seemingly Unrelated Regression,” The American Statistician”, 42(2), 137-139.
• OLAJIDE, Damilola ve LUDBROOK, Anne (2012),” Diet, Risk Of Obesity and Socioeconomic Circumstances Of Individuals in the UK: A Seemingly Unrelated Approach”, Nordic Journal of Health Economics, 1(2),100-118
• CHEN, Ching-Fu ve SOO, K. Tong (2007), “Cost structure and productivity growth of the Taiwanese international tourist hotels”,Tourism Management, 28, 1400-1407
• ÇIMAT, Ali ve BAHAR, Ozan (2003), “Turizm Sektörünün Türkiye Ekonomisi İçindeki Yeri ve Önemi Üzerine Bir Değerlendirme”, Akdeniz İ.İ.B.F. Dergisi, (6), 1-18
• ÇUHADAR, Murat (2013), “Türkiye’ye Yönelik Dış Turizm Talebinin MLP, RBF ve TDNN Yapay Sinir Ağı Mimarileri ile Modellenmesi ve Tahmini: Karşılaştırmalı Bir Analiz” , Journal of Yasar University, 8(31) 5274-5295
• DWIVEDI, T.D. and V.K. Srivastava (1978), “Optimality of Least Squares in the Seemingly Unrelated Regression Equations Model,” Journal of Econometrics, 7, 391-395
• FOSCHI, Paolo, KONTOGHIORGHES, Erricos.J.(2002), ”Seemingly unrelated regression model with unequal size observations:computational aspects”, Computational Statistics & Data Analysis, 41(1), 211 – 229
• FOSCHI, Paolo, KONTOGHIORGHES, Erricos .J.(2003), ” Estimating seemingly unrelated regression models with vector autoregressive disturbances”, Journal of Economic Dynamics & Control, 44, 3-35
• FOSCHI, Paolo, BELSLEY, David, A KONTOGHIORGHES, Erricos.J.(2003), ”A comparative study of algorithms for solving seemingly unrelated regressions models”, Computational Statistics & Data Analysis, 44, 3 – 35
• GREENE, H., William(2002), Econometric Analysis, Beşinci Baskı, New Jersey: Prentice Hall
• HAMADEH, Mohamad ve KHOUEIRI, Roy (2012), “Estimating The Demand For Tourism In Lebanon”, International Journal of Business and Economics Perspectives 7(1), 117-125
• HYUNGSIK, Roger Moon ve BENOİT, Perron (2006), “Seemingly Unrelated Regressions”, İnternet adresi: http://www.mapageweb.umontreal.ca/
• perrob/palgrave.pdf, Erişim Tarihi:24.02.2013
• HWANG, Hae-Shin (1990), “Estimation of Linear SUR Model with Unequal Numbers of Observations, Review of Economics and Statistics”, 72, 510-515.
• JACKSON, John. E. (2002), “A Seemingly Unrelated Regression Model for Analyzing Multiparty Elections”, Political Analysis, 10(1),49-65
• KESHAVARZI, Sareh, AYATOLLAHI, Seyyed M.T, ZARE, Najaf, FARKHONDEH, Sharif (2013),” Quality of life of childbearing age women and its associated factors: an application of seemingly unrelated regression (SUR) models”, Quality of Life Research, 22 (6), 1255-1263
• KMENTA, Jan (1986), Elements of Econometrics, Mcmillan Publishing Company
• KMENTA, Jan ve GILBERT, F., Roy (1968), “Small Sample Properties of Alternative Estimators of Seemingly Unrelated Regressions”, Journal of the American Statistical Association, 63 (324), 1180
• KONTOGHIORGHES, Erricos. J. ve CLARKE, M. R. B. (1995). "An alternative approach for the numerical solution of seemingly unrelated regression equations models," Computational Statistics & Data Analysis, Elsevier, vol. 19(4), pages 369-377
• KOZAK, Nazmi, KOZAK AKOĞLAN, Meryem ve KOZAK, Metin (2006), Genel Turizm İlkeler-Kavramlar, Ankara: Detay Yayıncılık
• MEHMETOĞLU, Mehmet (2010), “Factors Influencing the Willingness to Behave Environmentally Friendly at Home and Holiday Settings”, Scandinavian Journal of Hospitality and Tourism, 10(4), 430–447
• MEHTA, J.S. ve SWAMY, P.A.V.B. (1976): “Further evidence on the relative Estimating Seemingly Unrelated Regressions with First Order Autoregressive Disturbances effciencies of Zellners seemingly unrelated regressions estimator”, Journal of the American Statistical Association, 71 (355), 634-639.
• MILLIKEN, G.A. and M. ALBOHALI (1984), “On Necessary and Sufficient Conditions for Ordinary Least Squares Estimators to be Best Linear Unbiased Estimators,” The American Statistician, 38, 298-299.
• OKTAYER, Nagihan, SUSAM, Nazan ve ÇAK, Murat (2007), Türkiye’de Turizm Ekonomisi, İstanbul: İstanbul Ticaret Odası
• OLALI, Hasan ve TİMUR, Alp (1998), Turizm Ekonomisi, İzmir: Ofis Tica-ret Matbaacılık
• OLAMIDE, E.I ve ADEPOJU, A.A (2013), “Estimating Seemingly Unrelated Regressions with First Order Autoregressive Disturbances”, Studies in Mathematical Sciences, 6 (2), 40-57
• ÖNDER, A. Özlem, CANDEMİR, Aykan, KUMRAL, Neşe (2009),” An Empirical Analysis of the Determinants of International Tourism Demand: The Case of Izmir”, European Planning Studies, 17(10), 1525-1533
• PARKS, R. W. (1967), “Effcient estimation of a system of regression equations when disturbances are both serially and contemporaneously correlated”, Journal of the American Statistical Association, 62 (318), 500-509
• ROSSI, Peter E. (1989), “The ET Interview: Proffessor Arnold Zellner”, Econometric Theory, 5, 287-317
• REVANKAR, Nagesh.S. (1974), “Some Finite Sample Results in the Context of Two Seemingly Unrelated Regression Equations,” Journal of the American Statistical Association, 71, 183-188.
• RUFINO, Cesar C. (2011), “Forecasting International Demand for Philippine Tourism”, DLSU Business & Economics Review (21) 1, 61-76
• SALMAN, Khalik A. , ARNESSON, L., SORENSSON, A. ve SHUKUR, G.(2010), “Estimating International Tourism Demand for Selected Regions in Sweden and Norway with Iterative Seemingly Unrelated Regressions (ISUR)”, Scandinavian Journal of Hospitality and Tourism, 10 (4), 395–410
• SMITH, Michael ve KOHN, Robert, (2000). "Nonparametric seemingly unrelated regression," Journal of Econometrics”, vol. 98 (2), 257-281
• SRIVASTAVA, Virendra.K., D.E.A, Giles (1987), Seemingly Unrelated Regression Equations Models: Estimation And Inference, New York: Marcel Dekker
• SRIVASTAVA, Virendra.K. and T.D. Dwivedi (1979), “Estimation of Seemingly Unrelated Regression Equations: A Brief Survey,” Journal of Econometrics, 10, 15-32
• SONG, Haiyan, ve WITT,F., Stephen (2000), Tourism Demand Modelling and Forecasting: Modern Econometric Approaches, Oxford :Pergamon
• SONG, Haiyan ve LI, Gang (2008), “Tourism demand modelling and forecasting A review of recent research”, Tourism Management, 29(2), 203-220
• SONG,Haiyan,WITT,F., Stephen ve LI, Gang (2009), Advanced Econometrics of Tourism Demand, New York: Routledge
• SOYSAL, Mehmet ve ÖMÜRGÖNÜLŞEN, Mine (2010),” Türk Turizm Sektöründe Talep Tahmini Üzerine Bir Uygulama”, 21(1), 128-136
• TAKADA, H., ULLAH, A ve CHEN, Yu-Min (1995), “Estimation of the seemingly unrelated regression model when the error covariance matrix is singular”, Journal of Applied Statistics, 22 (4),517-530
• TACQ, Jacques (1997), Multivariate Analysis Techniques in Social Science Research, Sage.
• T.C. Devlet Planlama Teşkilatı, Turizm Özel İhtisas Komisyonu Raporu (2007), İnternet adresi: http://plan9.dpt.gov.tr/oik49_turizm/49turizm.pdf, Erişim tarihi 24.02.2013
• TIMM, H., Neil(2002), Applied Multivariate Analysis,New York: Springer
• UZGÖREN, Nevin (1996), Görünüşte İlişkisiz Regresyon Denklemlerinin Kestiriminde Kullanılan Yöntemlerin Etkinliklerinin Araştırılması -Türkiye İmalat Sanayiinde Bir Uygulama-, Osmangazi Üniversitesi İstatistik ABD Doktora Tezi
• VANEGAS Sr, Manuel (2009), ” Tourism Demand Response by Residents of Latin American Countries”, Internatıonal Journal Of Tourısm Research”, 11, 17-29
• WAN, Guang.H ve GRIFFITHS,W.E. (1992),”Estimation of risk effects with seemingly unrelated regressions and panel data”, Empirical Economics,17 (1), 35-49
• WITT, Stephan F. ve MARTIN, Christine. A. (1987), “Econometric models for forecasting international tourism demand”, Journal of Travel Research,25(3),23-30
• ZELLNER, Arnold (1962), “An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias”, Journal of the American Statistical Association, 57 (298), 348-368
• ZELLNER, Arnold ve HUANG (1962), S., David , “Further Properties of Efficient Estimators for Seemingly Unrelated Regression Equations”, International Economic Review, 3 (3), 300-313
• ZIRAMBA, Emmanuel ve MOYO, Busani (2013), “Aggregate Outbound Tourism Demand in South Africa: an Econometric Analysis”, Journal of Economics and Behavioral Studies, 5(5), 260-267