:::

詳目顯示

回上一頁
題名:國際觀光需求研究─開放政策、匯率與世界遺產之影響
作者:蘇鈺雯
作者(外文):Yu-Wen Su
校院名稱:臺灣大學
系所名稱:經濟學研究所
指導教授:林惠玲
學位類別:博士
出版日期:2013
主題關鍵詞:ARIMA介入模型排擠效果世界文化遺產實質匯率時間序列迴歸ARIMAIntervention analysisCrowding-out effectWorld Heritage SitesReal exchange ratesTime series regression
原始連結:連回原系統網址new window
相關次數:
  • 被引用次數被引用次數:期刊(0) 博士論文(0) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:0
  • 共同引用共同引用:0
  • 點閱點閱:9
本論文包含三篇觀光需求的實證研究。第一篇研究台灣開放中國觀光客的政策對於台灣國際觀光需求的影響;第二篇則以可變參數模型研究實質匯率對於台灣國際觀光需求的影響;第三篇主要探討世界遺產對於國際觀光需求的影響,並討論其邊際效果的變化。
第一章
Chinese Tourists in Taiwan: Changes in Visa Requirements, Crowding Effects and Management Implications
2008年七月,台灣開放中國觀光客來台觀光,本研究主要探討中國觀光客來台是否會排擠掉其他國家來台的觀光客。然而,由於缺少資料,我們首先使用中國觀光客到日本觀光的資料作為參考,以探討開放中國觀光客自由行對於觀光市場的衝擊。同時,在ARIMA模型設定下使用介入分析 (intervention analysis) 與離群值偵測 (outlier detection),針對來台的外國觀光客旅次,分析中國觀光客的排擠效果。結果顯示,即使台灣的觀光條件已逐步改善,中國觀光客對於來台的日本以及美國觀光客有顯著的排擠效果。顯示台灣目前的觀光條件仍不足,應再加以建設或是暫緩開放的速度。
第二章
Do Exchange Rates Affect International Tourist Arrivals in Taiwan? An Empirical Study Using Time-Varying Parameter Model
匯率對於觀光需求的影響在文獻上一直沒有定論,而傳統的時間序列迴歸分析總是假設參數固定,不會隨著時間而改變,然而,此假設相當不實際。觀察日本、香港及美國來台觀光旅次的資料,不僅是資料本身,其與其他變數的關係都有結構性的變動。因此,本研究使用可變參數模型 (time-varying parameter, TVP) 探討從1971年第一季至2011年第一季之間,實質匯率與台灣國際觀光需求間關係的穩定性。估計結果顯示,唯有當觀光客來源國的經濟景氣較差,或是其所得水準接近或低於台灣時,實質匯率對於其來台觀光需求有正向且顯著的影響。
第三章
Analysis of International Tourist Arrivals Worldwide: The Role of World Heritage Sites
本研究使用2000年至2009年間66個國家的資料,研究世界遺產 (world heritage sites, WHSs) 對於國際觀光需求的影響,並探討其邊際效果如何隨著擁有世界遺產數目的不同而改變。研究結果顯示,世界遺產對於國際觀光的確有正向且顯著的影響,且文化遺產的正向影響稍大於自然遺產,因此擁有世界遺產對於觀光經濟有所助益。此外,此正向影響會隨著擁有的世界遺產數目的增加而下降,但當一個國家擁有足夠多的世界遺產時,此影響又會再度增加,因此邊際影響隨著世界遺產數目的增加,呈現U型的影響效果。此效果雖具有地域性的差異,但是不同時間下的估計結果卻相當一致。
This dissertation includes three empirical studies on the tourism demand. The first studies the policy impact of Taiwan’s opening for Chinese tourists. The second explores the time-varying parameters model of the real exchange rate on Taiwan’s international tourism demand. The third investigates the piece-wise marginal effect of world heritage sites on the international tourism demand worldwide.
Chapter 1
Chinese Tourists in Taiwan: Changes in Visa Requirements, Crowding Effects and Management Implications
In July 2008, Taiwan passed legislation to allow Chinese tourists to travel to Taiwan. Even though this legislation has increased total inbound tourists, we are interested in potential crowding-out effects which may have a negative impact on Taiwan’s tourism. We analyze tourist arrivals from Japan, Hong Kong, and the United States to explore the crowding-out effect. Using seasonal ARIMA models with joint estimation of intervention and outlier effects, we find that Chinese tourists significantly crowd out Taiwan’s international tourists from Japan and the United States, but not those from Hong Kong, even with Taiwan’s increased tourism capacity. Therefore, our results indicate that Taiwan should either further enhance tourism capacity or decelerate its opening policy to avoid severe crowding-out effects.
Chapter 2
Do Exchange Rates Affect International Tourist Arrivals in Taiwan? An Empirical Study Using Time-Varying Parameter Model
There has been a debatable effect of the exchange rate on tourism demand. Not only the significance but the sign of the effect is questionable. Traditionally, time series regression model assumes parameters are constant over time, but this assumption is restrictive. For Taiwan’s time series data of international tourist arrivals from Japan, Hong Kong and the United States, not only tourist arrivals but also their relations with price factors would change structurally. Therefore, the time-varying parameter (TVP) approach is employed to explore the stability of influences of real exchange rates on Taiwan’s international tourist arrivals between 1971Q1 and 2011Q1. Allowing parameters varying by time, the estimated results indicate that the significantly positive effect of real exchange rates. However, this effect would occur only when the economic condition declines in source countries, or for whose income levels are close to or lower than Taiwan.
Chapter 3
Analysis of International Tourist Arrivals Worldwide: The Role of World Heritage Sites
By using the panel data of 66 countries between 2000 and 2009, we study the positive effect of world heritage sites (WHSs) on the demand for international tourism, and investigate how this effect changes according to different numbers of WHSs. Our results indicate that a country possessing WHSs would increase its international tourist arrivals, and the positive effect of natural WHSs is slightly larger than that of cultural ones. Therefore, a country possessing a WHS is able to benefit from the development of its tourism economy. Moreover, this positive effect declines as the number of WHSs rises, but when a country possesses sufficient WHSs, the effect increases instead. Thus, the marginal effect of WHSs exhibits a U-shaped pattern as the number of WHSs increases. In addition, even though the marginal effect of WHSs has a different pattern for each region, based on the time periods, the results are quite robust.
Adams, P.D. & Parmenter, B.R. (1995). An applied general equilibrium analysis of the economic effects of tourism in a quite small, quite open economy. Applied Economics, 27. 985-994.
Airey, D. & Chong, K. (2011). Tourism in China: policy and development since 1949. New York: Routledge.
Andrews, D.W.K. & Ploberger, W. (1994). Optimal tests when a nuisance parameter is present only under the alternative. Econometrica, 62, 1383-1414.
Andrews, D.W.K. (1993). Tests for parameter instability and structural change with unknown change point. Econometrica, 61, 821-856.
Artus, J.R. (1970). The effect of revaluation on foreign travel balance of Germany. IMF Staff Paper, 27, 601-617.
Artus, J.R. (1972). An econometric analysis of international travel. IMF Staff Paper, 19, 579-614.
Bell, W.R. & Hillmer, S.C. (1983). Modeling time series with calendar variation. Journal of the American Statistical Association, 78, 526-534.
Bille, T. & Schulze, G.G. (2008). Culture in urban and regional development. In V. A. Ginsburgh & D. Throsby (Eds.), Handbook of the economics of art and culture. Amsterdam: North Holland-Elsevier.
Bonet, L. (2003). Cultural tourism. In R. Towse (Ed.), A handbook of cultural economics. Cheltenham: Edward Elgar.
Box, G.E.P. & Jenkins, G.M. (1976). Time Series Analysis: Forecasting and Control. 2nd ed. Holden Day: San Francisco, CA.
Box, G.E.P. & Tiao, G.C. (1975). Intervention analysis with applications to economic and environmental problems. Journal of the American Statistical Association, 70, 70-79.
Cellini, R. (2011). Is UNESCO recognition effective in fostering tourism? A comment on Yang, Lin and Han. Tourism Management, 32, 452-454.
Chamberlain, G. (1984). Panel data. In Handbook of econometrics, Volume 2. Amsterdam: North Holland, 1247-1318.
Chan, F., Lim, C. & McAleer, M. (2005). Modelling multivariate international tourism demand and volatility. Tourism Management, 26, 459-471.
Chang, I., Tiao, G.C. & Chen, C. (1988). Estimation of time series parameters in the presence of outliers. Technometrics, 30, 193-204.
Chen, C. & Liu, L.M. (1993). Joint estimation of model parameters and outlier effects in time series. Journal of the American Statistical Association, 88, 284-297.
Chu, C.S., Hornik, K. & Kuan C.M. (1995). MOSUM tests for parameter constancy. Biometrika, 82, 603-617.
Chu, F.L. (2008). A fractionally integrated autoregressive moving average approach to forecasting tourism demand. Tourism Management, 29, 79-88.
Cooke, P. & Lazzaretti, L. (2008). Creative cities, cultural clusters and local economic development. Cheltenham: Edward Elgar.
Coshall, J.T. (2005). A selection strategy for modeling UK tourism flows by air to European destinations. Tourism Economics, 11, 1-158.
Crouch, G.L., Schultz, L. & Valerio, P. (1992). Marketing international tourism to Australia. Tourism Management, 13, 196-208.
Deng, J., King, B. & Bauer, T. (2002). Evaluating natural attractions for tourism. Annals of Tourism Research, 29(2), 422-438.
Dhariwala, R. (2005). Tourist arrivals in India: how important are domestic disorders? Tourism Economics, 11(2), 185-205.
Divisekera, S. (1995). An econometric model of international visitor floes to Australia. Australian Economic Papers, 34, 291-308.
Dougan, J.W. (2007). Analysis of Japanese tourist demand in Guam. Asia Pacific Journal of Tourism Research, 12(2), 79-88.
Dritsakis, N. (2004). Cointegration analysis of German and British tourism demand for Greece. Tourism Management, 25(1), 111-119.
Engle, R.F. (1982), Autoregressive conditional heteroskedasticity with estimates of the variances of United Kingdom inflation, Econometrica, 50, 987-1007.
Garin-Munoz, T. & Amaral, T.P. (2000). An econometric model for international tourism flow in Spain. Applied Economics Letters, 7, 525-529.
Gil-Alana, L.A. (2005). Modeling international monthly arrivals using seasonal univariate long-memory processes. Tourism Management, 26, 867-878.
Goh, C. & Law, R. (2002). Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention. Tourism Management, 23, 499-510.
Gray, P. (1966). The demand for international travel by the United States and Canada. International Economic Review, 7, 83-92.
Gunadhi, H. & Boey, C.K. (1986). Demand elasticities of tourism in Singapore. Tourism Management, 7, 239-253.
Hansen, B. (1997). Approximate asymptotic p values for structural-change tests. Journal of Business and Economic Statistics, 15, 60-67.
Hausman, J.A. (1978). Specification test in econometrics. Econometrica, 46, 1251-1271.new window
Herbert, D. (2001). Literary places, tourism and the heritage experience. Annals of Tourism Research, 28(2), 312-333.
Hillmer, S.C. (1982). Forecasting time series with trading day variation. Journal of Forecasting, 1, 385-395.
Huang, J.H. & Min, J.C.H. (2002). Earthquake devastation and recovery in tourism: The Taiwan case. Tourism Management, 23, 145-154.
Kalman, R.E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering (ASME), D85, 35-45.
Kim, J.H. & Moosa, I.A. (2001). Seasonal behavior of monthly international tourist flows: Specification and implications for forecasting models. Tourism Economics, 7, 381-396.
Kiran, B. (2010). The structure of tourism revenues in Turkey. Evidence from fractional integration under multiple structural breaks, Economic Studies Journal, 4, 85-96.
Kuan, C.M. & Hornik, K. (1995). The generalized fluctuation test: A unifying view. Econometric Reviews, 14, 135-161.new window
Kulendran, N. & Shan, J. (2002). Forecasting China''s monthly inbound travel demand. Journal of Travel & Tourism Marketing, 13, 5-19.
Ledesma-Rodriguez, F.J., Navarro-Ibanez, M. & Perez-Rodriguez, J.V. (2001). Panel data and tourism: a case study of Tenerife. Tourism Economics, 7, 75-88.
Lee, K., Var, T. & Blaine, T.W. (1996). Determinants of inbound tourist expenditures. Annals of Tourism Research, 23, 527-542.
Li, G., Song, H. & Witt, S.F. (2006). Time varying parameter and fixed parameter linear AIDS: An application to tourism demand forecasting. International Journal of Forecasting, 22, 57-71.
Li, G., Wong, K.F., Song, H. & Witt, S.F. (2006). Tourism demand forecasting: A time varying parameter error correction model. Journal of Travel Research, 45, 175-185.
Li, M., Wu, B. & Cai, L. (2008). Tourism development of world heritage sites in China: a geographic perspective. Tourism Management, 29, 308-319.
Light, D. (2000). Gazing on communism: heritage tourism and post-communist identities in Germany, Hungary and Romania. Tourism Geographies, 2(2), 157-176.
Lim, C. (1997). Review of International Tourism Demand Models. Annals of Tourism Research, 24, 835-849.
Lim, C. (2006). A survey of tourism demand modeling practice: issues and implications. International Handbook on the Economics of Tourism, Cheltenham: Edward Elgar, 45-72.
Lim, C. & McAleer, M. (2000). A seasonal analysis of Asian tourist arrivals to Australia. Applied Economics, 32, 499-509.
Lim, C. & Wang, Y., (2008). China''s post-1978 experience in outbound tourism. Mathematics and Computers in Simulation, 78, 450-458.
Lin, H.L., Liu, L.M., Tseng, Y.H. & Su, Y.W. (2011). Taiwan''s international tourism: A time series analysis with calendar effects and joint outlier adjustments. International Journal of Tourism Research, 13, 1-16.
Little, J.S. (1980). International travel in the UK balance of payments. New England Economic Review, May, 42-55.
Liu, L.M. & Hanssens, D.M. (1982). Identification of multiple-input transfer function models. Communications in Statistics, A11, 297-314.
Liu, L.M. (1980). Analysis of time series with calendar effects. Management Science, 26, 106-112.
Liu, L.M. (2006). Time Series Analysis and Forecasting. 2nd ed. Scientific Computing Associates Corp.: Chicago, IL.
Liu, L.M. & Chen, C. (1991). Recent developments of time series analysis in intervention in environmental impact studies. Journal of Environmental Science and Health, A26, 1217-1252.
Liu, L.M. & Hudak, G.B. (1992). Forecasting and Time Series Analysis Using the SCA Statistical System, Vol 1. Scientific Computing Associates Corporation: Chicago, IL.
Loeb, P.D. (1982). International travel to the United States: An econometric evaluation. Annals of Tourism Research, 9, 7-20.
Maloney, W.F. & Montes-Rojas, G.V. (2005). How elastic are sea, sand and sun? Dynamic panel estimates of the demand for tourism. Applied Economics Letters, 12, 277-280.
Martin, C.A. & Witt, S.F. (1988). Substitute prices in models of tourism demand. Annals of Tourism Research, 15, 255-268.
McCool, S.F. & Martin, S.R. (1994). Community attachment and attitudes toward tourism development. Journal of Travel Research, 32(3), 29-34.
McIntosh, A. & Prentice, R. (1999). Affirming authenticity: consuming cultural heritage. Annals of Tourism Research, 26, 589-612.
Min, J.C.H. (2005). The effect of the SARS illness on tourism in Taiwan: An empirical study. International Journal of Management, 22, 497-506.
Naude, W.A. & Saayman, A. (2005). Determinants of tourist arrivals in Africa: A panel data regression analysis. Tourism Economics, 11, 365-391.
Pan, G.W. (2003). A theoretical framework of business network relationships associated with the Chinese outbound tourism market to Australia. Journal of Travel Tourism Market, 14(2), 87-104.
Papatheodorou, A. & Song, H. (2005). International tourism forecasts: Time-series analysis of world regional data. Tourism Economics, 11, 11-23.
Patsouratis, V., Frangouli, Z. & Anastasopoulos, G. (2005). Competition in tourism among the Mediterranean countries. Applied Economics, 37, 1865-1870.
Payne, J.E. & Mervar, A. (2002). A note on modeling tourism revenues in Croatia. Tourism Economics, 8, 103-109.
Ploberger, W. & Kramer, W. (1992). The CUSUM test with OLS residuals. Econometrica, 60, 271-285.new window
Quayson, J. & Var, J. (1982). A tourism demand function for the Okanagan, BC. Tourism Management, 3, 108-115.
Rosensweig, J.A., (1986). Exchange rate and competition for tourists. New England Economic Review, 7/8, 57-67.
Song, H. & Li, G. (2008). Tourism demand modeling and forecasting: a review of recent research. Tourism Management, 29, 203-230.
Song, H. & Witt, S.F. (2000). Tourism demand modeling and forecasting: modern econometric approaches. Oxford: Pergamon.
Song, H. & Wong, K.K.F. (2003). Tourism demand modeling: A time-varying parameter approach. Journal of Travel Research,42, 57-64.
Song, H., Li, G., Witt, S.F. & Athanasopoulos, G. (2011). Forecasting tourist arrivals using time-varying parameter structural time series models. International Journal of Forecasting, 27, 855-869.
Song, H., Witt, S.F. & Jensen, T.C. (2003). Tourism forecasting: Accuracy of alternative econometric models. International Journal of Forecasting, 19, 123-141.
Sriboonchitta, S., Chaitip, P., Balogh, P., Kovacs, S. & Chaiboonsri, C. (2011). On tests for long memory process behavior of international tourism market:Thailand and India, Applied Studies in Agrobusiness and Commerce, 5, 95-100.
Su, Y.W., Lin, H.L. & Liu, L.M. (2012). Chinese tourists in Taiwan: Crowding out effects, opening policy and its implications. Tourism Management Perspectives, 4, 45-55.
Tan, A.Y.F., McCahon, C. & Miller, J. (2002). Modeling tourist flows to Indonesia and Malaysia. Journal of Travel and Tourism Marketing, 13, 61-82.
Tremblay, P. (1989). Polling international tourism in Western Europe. Annals of Tourism Research, 16, 477-491.
Tsay, R. (1988). Outliers, level shifts, and variance changes in time series. Journal of Forecasting, 7, 1-20.
Wang, Y.S. (2009). The impact of crisis events and macroeconomic activity on Taiwan''s international inbound tourism demand. Tourism Management, 30, 75-82.
Webber, A.G. (2001). Exchange rate volatility and co-integration in tourism demand. Journal of Travel Research, 39, 398-405.
Witt, S.F. & Witt, C.A. (1995). Forecasting tourism demand: A review of empirical research. International Journal of Forecasting, 11, 447-475.
Witt, S.F., Song, H. & Louvieris, P. (2003). Statistical testing in forecasting model selection. Journal of Travel Research, 42, 151-158.
Wooldridge, J.M. (2002). Basic linear unobserved effects panel data models. In Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press, 247-291.
Yang, C.-H. & Lin, H.-L. (2011). Is UNESCO recognition effective in fostering tourism? A comment on Yang, Lin and Han: Reply. Tourism Management, 32, 455-456.
Yang, C.-H., Lin, H.-L. & Han, C.-C. (2010). Analysis of international tourist arrivals in China: the role of world heritage sites. Tourism Management, 31, 827-837.
Zhang, H.Q. & Heuang, V.C.S. (2001). The emergence of the mainland Chinese outbound travel market and its implications for tourism marketing. Journal of Vacation Marketing, 8(1), 7-12.

 
 
 
 
第一頁 上一頁 下一頁 最後一頁 top