Seasonality is an important feature of the tourism industry, and one of the greatest disturbing factors affecting related business operation in the industry. This study used the structural time series model to analyze the seasonality of the accommodation demand for tourist hotels, and this method can test the stochasticity of seasonality and observe the seasonal pattern changes, which makes itself a very good way to explore the seasonality of tourism. The empirical results show that the accommodation demands for tourist hotels in all 7 areas are all obviously seasonal and stochastic. The seasonal pattern of tourist hotel accommodation demand of Taipei area, and Taoyuan, Hsinchu, and Miaoli area are similar; the pattern of Kaohsiung area and Taichung area are also similar to each other; and Hualien area, scenic area and other areas are similar in their seasonal pattern, mainly due to the difference in the structure of tourist sources. Faced with the seasonal changes, hoteliers can adopt the strategy of price differentiation, cooperate with other industries and hold events, conferences and exhibitions with the local government to expand the tourist source and increase the room occupancy rate.

El objetivo del presente artículo es analizar la relación entre la demanda de transporte y el crecimiento económico en el Perú. Para ello se realiza la prueba de no causalidad de Toda y Yamamoto seguido de la estimación de un Modelo Autorregresivo de Rezago Distribuido (ARDL) que analiza las variables de demanda de la infraestructura aeroportuaria, ferroviaria, portuaria, así como los kilómetros pavimentados de carreteras a nivel nacional con el crecimiento económico. Los resultados muestran que es el crecimiento económico el que impulsa la demanda de transporte, con excepción de la demanda aeroportuaria en donde se encontró una relación de causalidad bidireccional entre la demanda de pasajeros y de carga con el crecimiento económico. De esta manera, ante un incremento de 1% en el Producto Bruto Interno (PIB) la demanda de carga aeroportuaria incrementa en 0,728%, la demanda de pasajeros aeroportuarios en 2,536%, los de kilómetros pavimentados de carreteras en 1,324%; y la demanda de pasajeros ferroviarios así como la demanda de carga portuaria se incrementan en 0,571% y 1,243%, respectivamente. Por su parte, un incremento de 1% en la demanda aeroportuaria de carga genera un incremento de 0,778% en el PIB, mientras que un incremento en la demanda de pasajeros en 1% conlleva a un aumento de 0,334% en el PIB. Por otro lado, se encontró evidencia que las Asociaciones Público Privadas en carreteras han generado un impacto positivo y significativo en la economía.

No abstract

<p>Seasonality is one of the significant features in tourism market. This study employs the X-13 ARIMA-SEATS method to tourism market in Taiwan. Tourists who had come to Taiwan from 1981 to 2016 mainly came from Asia, followed by the Americas and Europe. In Asian area, tourists from Mainland China account for the highest percentage, followed by Hong Kong and Japan, whose overall resources provide favourable conditions for industrial development. Rapid growth in the number of tourists coming to Taiwan gives rise to the issue of uniform distribution of tourists during the year, namely, tourism seasonality. The empirical results show that tourism seasonality of tourists coming to Taiwan is randomly changing. Analysis should be conducted concerning sustainable planning, environmental dynamic carrying capacity and sustainable development. The high tourism seasons are March, April, November and December. However, January, July and September every year are off-season in Taiwan’s tourism market, with gradual decreasing number of tourists compared with those in high-season months. The contribution of this research is the analysis of data from high-season and off-season months, The local transport routes and environmental facilities can be planned for the high-season months, in order to develop diversified tourism marketing and strategies, improve the utilisation of space, and enhance business performance. During off-season months, Stay at Home Economic may be developed through Internet or platform marketing to provide distance-free remote services. For the overall environment, The analysis between off-season and high-season months not only helps to generate economic development, but can provide a sustainable planning direction, and link environmental dynamic carrying capacity and sustainable development. </p>