Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/45722
標題: AGGREGATION, HETEROGENEOUS AUTOREGRESSION AND VOLATILITY OF DAILY INTERNATIONAL TOURIST ARRIVALS AND EXCHANGE RATES
作者: CHANG, CHIA-LIN
MCALEER, MICHAEL
摘要: Tourism is a major source of service receipts for many countries, including Taiwan. The two leading tourism countries for Taiwan are Japan and the USA, which are sources of short- and long-haul tourism, respectively. As a strong domestic currency can have adverse effects on international tourist arrivals through the price effect, daily data from 1 January 1990 to 31 December 2008 are used to model the world price, exchange rates, and tourist arrivals from the world, the USA and Japan to Taiwan, and their associated volatility. Inclusion of the exchange rate and its volatility captures approximate daily and weekly price and price volatility effects on world, US and Japanese tourist arrivals to Taiwan. The heterogeneous autoregressive model is used to approximate the slowly decaying correlations associated with the long-memory properties in daily and weekly exchange rates and international tourist arrivals, to test whether alternative short- and long-run estimates of conditional volatility are sensitive to the long-memory in the conditional mean, to examine asymmetry and leverage in volatility, and to examine the effects of temporal and spatial aggregation. The approximate price and price volatility effects tend to be different, with the exchange rate typically having the expected negative impact on tourist arrivals to Taiwan, whereas exchange rate volatility can have positive or negative effects on tourist arrivals to Taiwan. For policy purposes, the empirical results suggest that an arbitrary choice of data frequency or spatial aggregation will not lead to robust findings as they are generally not independent of the level of aggregation used.
URI: http://hdl.handle.net/11455/45722
文章連結: http://dx.doi.org/10.1111/j.1468-5876.2011.00563.x
Appears in Collections:應用經濟學系

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