Recent Developments in Econometric Modeling and Forecasting

Abstract

Eighty-four post-1990 empirical studies of international tourism demand modeling and forecasting using econometric approaches are reviewed. New developments are identified and it is shown that applications of advanced econometric methods improve the understanding of international tourism demand. An examination of the 22 studies which compare forecasting performance suggests that no single forecasting method can outperform the alternatives in all cases. However, the timevarying parameter (TVP) model and structural time series model with causal variables perform consistently well.

Tourism Demand Modeling and Forecasting: A Review of Literature Related to Greater China

“…Witt and Witt (1995) reviewed 40 empirical studies published prior to 1992, with a particular emphasis on empirical comparisons of forecasting accuracy among different approaches. Li et al (2005) and Song and Li (2008) reviewed the most recent 84 and 121 tourism demand studies, respectively. They identified the latest methodological developments and emerging trends in this field of research.…”

Measuring price elasticities of demand for outbound tourism using competitiveness indices

“…The resulting set of prices, if included separately, will result in an 'intractable' model (Crouch, 1994). As a consequence, composite prices in the form of ratios of prices at the destination to those of the home countries are constructed and used (Li et al, 2005). The underlying assumption is that the potential traveller will compare these prices in local currency and prices prevailing in the home country when making the travel decision.…”

A combination selection algorithm on forecasting

“…The VACO method is a special case of the DMSFE when  = 1.  is chosen as 0.95, 0.9, 0.85 and 0.8, respectively which is the same as in the papers (Li, Song & Witt, 2005; All the above combination methods, SA, VACO and DMSFE, are linear combinations as discussed by Shen, Li and Song (2008) and Wong et al (2007). The advantage of these combinations is that they are simple and easy to apply.…”

“…Three main types of forecasting models (Li, Song & Witt, 2005; are Time series model (Cao, Ewing & Thompson, 2012;Cho, 200;Goshall & Charlesworth, 2011), Causal econometric model (Li, Song & Witt, 2006;Naude & Saayman, 2005;Page, Song & Wu, 2012;Roget & Gonzalez, 2006) and new emerging Artificial Intelligence based model, such as neural network, fuzzy time-series theory, grey theory, genetic algorithms, and expert systems (Cao, Ewing & Thompson, 2012;Carbonneau, Laframboise & Vahidov, 2008;Bodyanskiy & Popov 2006;Chen & Wang, 2007;Cho, 2003;Hadavandi, Ghanbari , Shahanaghi & Abbasian-Naghneh, 2011;Law & Au, 1999;Pai & Hong, 2005;Wong, Xia & Chu, 2010;Wu & Akbarov, 2011). From these studies, researchers often seek to identify the best individual model to generate a forecast.…”