2. Inbound Tourism Demand Modelling; Gothenburg case
• I. Introduction & Literature Reviewed
• II. Theoretical Framework
Determinants of Tourism demand function and other
aspects of Tourism demand modelling
• III. Methodology and Diagnostic test procedure
• IV. Results and Analysis
Data and summary statistics
Results from the International Markets
Results from the National Regional Markets
• V. Conclusions
3. Introduction
• The research project focus on the description of the determinants and the
tourism demand function in Gothenburg region as destination,
– Measured by guest night production and
– The general to specific modelling approach from the top 5 markets:
Sweden, Norway, UK, USA and Germany.
• I have worked with a time series annually data from 1982 to 2013,
– Ordinary least squares (OLS)
– Autoregressive distributed lag model (ADLM)
Inbound Tourism Demand Modelling; Gothenburg case
5. Literature Reviewed
• The gap in the literature reviewed;
– Lack of diagnostic checking in the empirical studies.
– The supply side of the market is not the favorite research topic for modelling
and forecasting tourism.
– New software improvements make possible to work with the computable
modelling approach.
Inbound Tourism Demand Modelling; Gothenburg case
6. Theoretical Framework
Inbound Tourism Demand Modelling; Gothenburg case
Determinants of tourism demand function
• The inbound demand function for the tourism product in destination
• i = Gothenburg, by residents of origin j = USA, UK, Germany, Norway
and Sweden, is given by
Qij = f (Yj, Pij, Psj)
Yj is the level of income in origin country j
Pij is the price of tourism in destination i, for residents in origin country j
Psj is the price of tourism in the substitute destination s, for resident in
origin country j
7. Tourism Demand Modelling
• The most basic relation between the variables is the linear relationship,
Song and Witt (2000) and Song, Wong and Chong (2003).
Qjt = α0 + α1Yit + α2Pjt + α3Psjt + ęit
• Relative consumer price of tourism in Sweden for International market
Pijt = (CPISW/EXSW)/(CPIj/EXj), j = 1, 2, 3, 4
• Relative consumer price of tourism in Sweden for Domestic Market
Pit = (CPISW/EXSW), t = 1982, …, 2013
• Relative consumer price of tourism in Norway for International Market
Psjt = (CPInw/EXnw)/(CPIj/EXj), j = 1, 2, 3, 4
Inbound Tourism Demand Modelling; Gothenburg case
8. Methodology
The “traditional” tourism demand modelling proceeds with the following
steps:
a) Formulate hypotheses based on classic microeconomic theory
b) Through the decided model´s functional form, present data and
estimate the coefficients
c) Once estimate the model´s coefficients, generate forecast for the
destination using the Elasticity concept:
η = (α)*(Prj/Qjt)
Inbound Tourism Demand Modelling; Gothenburg case
9. • Hypothesis I: The Engel curve suggest that if the price of tourism is held constant,
an increase in tourists´ income will result in an increase in the demand for tourism
to the destination provided: tourism is a normal or necessary good
• Hypothesis II: If the price of tourism in destination 1 increases while price of
tourism in destination 2 and consumers´ income in the origin country remain
unchanged, tourist will “switch” from going to destination 1 to destination 2, and
therefore the demand for tourism to destination 1 will decrease
• Hypothesis III: With respect to the demand for tourism to destination 1, the effect
of a price change in destination 2 can have either, a positive or negative effect
– If destination 2 is substitute for destination 1 the demand for tourism to destination 1 will
move in the same direction as to the price change in destination 2.
– if tourist tend to travel both destinations together, i.e., the destination are complementary to
each other, tourism demand to one destination will move in the opposite direction to the
change in price of tourism in the other
Hypotheses
Inbound Tourism Demand Modelling; Gothenburg case
10. • The inbound tourism demand equation
The static model by origin country, OLS regression
qjt = βo + β1 Yjt + β2 Pjt + β3 Psjt + ε it j=1, 2, 3, 4, 5
The ADLM model by origin country, Newey-West regression
qjt = α0 + α1Yjt + α2Yjt-1 + α3Pjt + α4Pjt-1 + α5 Pst + α6 Pst-1 + υjt
j=1, 2, 3, 4, 5
Inbound Tourism Demand Modelling; Gothenburg case
Diagnostic test procedure
11. • OLS; Best Linear Un-bias Estimator BLUE
– Gauss-Markov assumptions
– t-test and p-value, significance level
• Lagrange Multiplier; autocorrelation
– Breusch (1978) and Godfrey (1978)
– Variance – Covariance matrix
• Newey-West
– Autocorrelation
– Heteroscedasticity
– Spurious regression
• Unit root test - Cointegration
Dilemma on instruments, How to measure?
Inbound Tourism Demand Modelling; Gothenburg case
12. Ordinary least squares, OLS
• BLUE-Gauss Markov
– A1: E(ui|Xi)=0
– A2: (Xi,Yi) are independent thus (Xi,ui) are
independent (endogeneity)
– A3: Var (ui) = σ2 (homoskedasticity)
– A4: Cov (Ui,Uj) = 0, (no-autocorrelation)
Inbound Tourism Demand Modelling; Gothenburg case
14. Measures of fit; R-square
• R2 = ESS/ TSS
– Explained sum of squares/Total sum of squares
• R2 = 1 – (SSR/ TSS)
– Sum of squares residuals/Total sum of squares
20. • An important observation is that the ADLM performs stable
the regional-domestic forecast.
• I can describe that 1% change increase in the relative consumer price of
tourism in Sweden, will decrease the domestic demand 0.7% or 20,931
guest night production in 2014.
• Furthermore, 1% change increase in the relative consumer price of
tourism in Norway, will decrease the domestic demand in 0.08% the guest
night production.
• Regarding to the regional market with Norway, I can describe that 1%
change increase in the relative consumer price of tourism in Sweden for
Norwegians, decrease the travelers´ demand to Gothenburg 0.35% or 921
guest night production in 2014.
Results
Inbound Tourism Demand Modelling; Gothenburg case
21. Step forward
• An important conclusion is that the ADLM is not stable in the international
forecast. The positive aspect inside this negative outcome is related to the distance
factor.
– Likely to deal with omitted variable bias related to travel costs and distance from the
destination point
• Potential solutions, Song, Wong and Chong 2003:
– Aggregate the seasonality effects through the monthly or quarterly database, use TVP as
instrument of estimator
– Re-design the relative consumption price of tourism in the destinations
– Use an alternative ratio relation in the substitute destination as follows;
Psjt = (CPInw/EXnw) wj , where, wj = (TGNsj / Σsj=1,2 TGNsj)
• W is the proportion of international guest nights production from origin country j into the substitution
destination, related with the summary of guest nights production of the two destinations from the same origin
country j.
Inbound Tourism Demand Modelling; Gothenburg case
22. Inbound Tourism Demand Modelling; Gothenburg case
Antal gästnätter per månad, 2008 – 2014, SCB.
Millions gästnätter
0
0.5
1
1.5
2
2.5 2008M01
2008M04
2008M07
2008M10
2009M01
2009M04
2009M07
2009M10
2010M01
2010M04
2010M07
2010M10
2011M01
2011M04
2011M07
2011M10
2012M01
2012M04
2012M07
2012M10
2013M01
2013M04
2013M07
2013M10
2014M01
2014M04
2014M07
2014M10
2015M01
2015M04
Millions
Stor-Göteborg Västra Götalands län VGR-SG
