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on Tourism Economics |
| By: | Kannan, Srinivasan |
| Abstract: | Tourism is a service sector which earns a substantial foreign exchange to developing countries. In India, Kerala is one of the important destination for the international tourists with its unique nature beauty with backwaters, mountains and beaches. To make the tourism a great success one has to take advantage of the modern technology to full extent. Present paper is an attempt to market tourism by adapting the service marketing approach for achieving great success. |
| Keywords: | Tourism; Service Marketing; 8 Ps; Marketing Mix |
| JEL: | M31 |
| Date: | 2009 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:14031&r=tur |
| By: | George Athanasopoulos; Rob J Hyndman; Haiyan Song; Doris C Wu |
| Abstract: | We evaluate the performance of various methods for forecasting tourism demand. The data used include 380 monthly series, 427 quarterly series and 530 yearly series, all supplied to us by tourism bodies or by academics from previous tourism forecasting studies. The forecasting methods implemented in the competition are univariate time series approaches, and also econometric models. This forecasting completion differs from previous competitions in several ways: (i) we concentrate only on tourism demand data; (ii) we include econometric approaches; (iii) we evaluate forecast interval coverage as well as point forecast accuracy; (iv) we observe the effect of temporal aggregation on forecasting accuracy; and (v) we consider the mean absolute scaled error as an alternative forecasting accuracy measure. |
| Keywords: | Tourism forecasting, ARIMA, Exponential smoothing, Time varying parameter model, Autoregressive distributed lag model, Vector autoregression |
| JEL: | C22 C52 C53 |
| Date: | 2008–12 |
| URL: | http://d.repec.org/n?u=RePEc:msh:ebswps:2008-10&r=tur |
| By: | Chia-Lin Chang (Department of Applied Economics, National Chung Hsing University, Taiwan); Michael McAleer (School of Economics and Commerce, University of Western Australia); Dan Slottje (FTI Consulting and Department of Economics, Southern Methodist University) |
| Abstract: | International tourism is a major source of export receipts for many countries worldwide. Although it is not yet one of the most important industries in Taiwan (or the Republic of China), an island in East Asia off the coast of mainland China (or the People's Republic of China), the leading tourism source countries for Taiwan are Japan, followed by USA, Republic of Korea, Malaysia, Singapore, UK, Germany and Australia. These countries reflect short, medium and long haul tourist destinations. Although the People's Republic of China and Hong Kong are large sources of tourism to Taiwan, the political situation is such that tourists from these two sources to Taiwan are reported as domestic tourists. Daily data from 1 January 1990 to 30 June 2007 are obtained from the National Immigration Agency of Taiwan. The Heterogeneous Autoregressive (HAR) model is used to capture long memory properties in the data. In comparison with the HAR(1) model, the estimated asymmetry coefficients for GJR(1,1) are not statistically significant for the HAR(1,7) and HAR(1,7,28) models, so that their respective GARCH(1,1) counterparts are to be preferred. These empirical results show that the conditional volatility estimates are sensitive to the long memory nature of the conditional mean specifications. Although asymmetry is observed for the HAR(1) model, there is no evidence of leverage. The QMLE for the GARCH(1,1), GJR(1,1) and EGARCH(1,1) models for international tourist arrivals to Taiwan are statistically adequate and have sensible interpretations. However, asymmetry (though not leverage) was found only for the HAR(1) model, and not for the HAR(1,7) and HAR(1,7,28) models. |
| Date: | 2009–02 |
| URL: | http://d.repec.org/n?u=RePEc:pad:wpaper:0097&r=tur |
| By: | Jae H. Kim; Haiyang Song; Kevin Wong; George Athanasopoulos; Shen Liu |
| Abstract: | This paper evaluates the performance of prediction intervals generated from alternative time series models, in the context of tourism forecasting. The forecasting methods considered include the autoregressive (AR) model, the AR model using the bias-corrected bootstrap, seasonal ARIMA models, innovations state-space models for exponential smoothing, and Harvey's structural time series models. We use thirteen monthly time series for the number of tourist arrivals to Hong Kong and to Australia. The mean coverage rate and length of alternative prediction intervals are evaluated in an empirical setting. It is found that the prediction intervals from all models show satisfactory performance, except for those from the autoregressive model. In particular, those based on the bias-corrected bootstrap in general perform best, providing tight intervals with accurate coverage rates, especially when the forecast horizon is long. |
| Keywords: | Automatic forecasting, Bootstrapping, Interval forecasting |
| JEL: | C22 C52 C53 |
| Date: | 2008–12 |
| URL: | http://d.repec.org/n?u=RePEc:msh:ebswps:2008-11&r=tur |
| By: | Franziska Kupfer (University of Antwerp, Department of Transport and Regional Economics); Frédéric Lagneaux (National Bank of Belgium, Microeconomic Information Department) |
| Abstract: | This study is a publication issued by the Microeconomic Analysis service of the National Bank of Belgium, in partnership with the Department of Transport and Regional Economics of the University of Antwerp (UA). It is the outcome of a first research project on the Belgian airport and air transport sector. The former relates to the economic activities within the airports of Antwerp, Brussels, Charleroi, Kortrijk, Liège and Ostend, while the latter concentrates on the air transport business as a whole. In the past few years, the logistics business has come to play a significant part in income creation in our country, whose economy is to a large extent driven by services1. Air transport and airports in particular are driving forces in this context, not only in terms of business generated within the air transport cluster, but also in terms of airports' attractiveness. On world scale an overall growth of cargo and passengers could be observed in the last ten years. However, the air transport sector has undergone a major crisis during the 2001-2003 period, when passenger traffic numbers first fell sharply and then stagnated. Only after 2003 this activity has picked up again and this until the third quarter of 2008. Cargo traffic on its part recovered already in 2002. In Belgium, a similar evolution can be observed. It should be stressed however that between 1997 and 2007 cargo volumes grew much faster than passenger traffic did. The rankings of European airports underline the importance of cargo traffic for Belgium: In 2006 Brussels, Liège and Ostend-Bruges respectively occupy ranks 6, 8, and 20 in the European cargo airport top 20, while for passenger airports, Brussels can only be found at the end of the top 20. In this study, a sectoral approach has been followed by focusing, for every airport, on two major economic activity components: the air transport cluster on the one hand and other airport-related sectors on the other hand. In that respect, annual accounts data from the Central Balance Sheet Office were used for the calculation of direct effects, the social balance sheet analysis and the study of financial ratios. Due to an inevitable time lag in the data provision, the analysis was limited to 2006. Like in other sectoral studies published by the Bank, indirect effects have also been estimated on the basis of data from the National Accounts Institute. In 2006, the total activities under review –direct and indirect, inside and outside airports- accounted for roughly 6.2 billion euro, i.e. 2 p.c. of Belgium's GDP and domestic employment. Considering the direct effects only, these percentages both amounted to 0.8 p.c. The three major airports, i.e. Brussels, Charleroi and Liège, alone account for 95.2 p.c. of the direct value added generated by the six airports under review. They represent 0.5 p.c. of Belgian GDP and, taking account of the indirect effects, 1.1 p.c. of the national income. Furthermore, it has to be pointed out that most Belgian airports are specialised. While the airports of Liège and Ostend focus on air cargo, Charleroi Airport deals mostly with low-cost passenger transport. Moreover, the smaller regional airports like Antwerp and Kortrijk focus on business travel. The analysis was completed in December 2008 |
| Keywords: | air transport, airport activities, sector analysis, indirect effect, public investments UA, |
| JEL: | C67 D40 J21 L93 R15 R34 R41 |
| Date: | 2009–03 |
| URL: | http://d.repec.org/n?u=RePEc:nbb:docwpp:200903-03&r=tur |
| By: | Rajagopal (Tecnológico de Monterrey, Campus Ciudad de México) |
| Abstract: | Shopping malls contribute to business more significantly than traditional markets which were viewed as simple convergence of supply and demand. Shopping malls attract buyers and sellers, and induce customers providing enough time to make choices as well as a recreational means of shopping. However, competition between malls, congestion of markets and traditional shopping centers has led mall developers and management to consider alternative methods to build excitement with customers. This study examines the impact of growing congestion of shopping mall in urban areas on shopping conveniences and shopping behavior. Based on the survey of urban shoppers, the study analyzes the cognitive attributes of the shoppers towards attractiveness of shopping malls and intensity of shopping. The results of the study reveal that ambiance of shopping malls, assortment of stores, sales promotions and comparative economic gains in the mall attract higher customer traffic to the malls. |
| Keywords: | Shopping malls, traditional markets, sales promotion, market ambiance, leisure shopping, recreational services, retailing, market congestion, customer value, consumer behavior |
| JEL: | D12 L81 M31 R51 |
| Date: | 2009–03 |
| URL: | http://d.repec.org/n?u=RePEc:ega:wpaper:200805&r=tur |