IDENTIFYING AND MODELLING THE

MAIN MACRO DRIVERS OF TOURISM

DEMAND WITH MATLABIman Behzadian

Senior Data Scientist

Tourism and Events Queensland

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Problem Definition

Why are econometric models important?

Developing a Framework

Methodology/Results

How did MATLAB help?

Make Insightful Decision Based on The Results

Contents

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Modelling The Effects of the Macro Drivers of a

Source Market on Tourism Demand

Quantitative Analysis

What are the key drivers of tourism demand

What are their relationships?

What are the best proxies for them?

What are the relative importance of these drivers

Problem Definition

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the need for an analysis of the adequacy of model specifications and the statistical deficiencies of

existing empirical tourism demand models is sensed in this area.

Why is it important to validate the model?

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Choosing the right Technique depend on the nature of the problem.

Some of the Modelling techniques have higher explanatory power than others. Some are more accurate in terms of predicting the future.

Modelling Techniques

Parametric Modelling

•Curve Fitting

•Linear Regression Techniques

Machine Learning

•Supervised

•Unsupervised

Dynamic Modelling

•ARIMA

•GARCH

•VAR

•SDE

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How Does Matlab Help?

Parametric Modelling

•Curve Fitting

•Linear Regression Techniques

Machine Learning

•Supervised

•Unsupervised

Dynamic Modelling

•ARIMA

•GARCH

•VAR

•SDE

• Curve Fitting Toolbox

• Statistics

and Machine Learning Toolbox

• Statistics and Machine Learning

Toolbox

• Neural Networks Toolbox

• DatafeedToolbox

• Database Toolbox

• Econometrics Toolbox

• Optimization toolbox

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Selecting The Major Macro Drivers

Selecting Proper Proxies for Each

Preparing The Data Tuning VAR Model

Checking Impulse

Response Analysis and Back Testing

Results

Developing a Framework (VAR)

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Methodology

Selecting Proxy

Preparing Data

Tuning

Model

Parameters

Checking the results

Granger causality Test (Based on T-test) can be a good indicator

Tourism Demand

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Unit Root Test for Stationarity

ARCH Effects Test for Residual Autocorrelation

Making The Data Stationary

Removing Seasonality

Adjusting the Shifts occurred due to This Process

Methodology

Selecting Proxy

Preparing Data

Tuning

Model

Parameters

Checking the results

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Methodology

Selecting Proxy

Preparing Data

Tuning

Model

Parameters

Checking the results

Simple AutoCorrFunction for AR term of ARIMA or VAR model

Partial AutoCorrFunction for MA term of ARIMA or VARMA

Simple and Partial

AutoCorr Function on the squared version of signal for GARCH model

Seasonality

A djus tment

Needed

Suitable for VAR (n=2)

model

Diff

Needed

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Results

Selecting Proxy

Preparing Data

Tuning

Model

Parameters

Checking the results

The Econometric model is developed to estimate monthly relationship between total inbound tourism spending in Australia from each source market and the following explanatory variables.

Economic Activity and wealth as proxied by local stock exchange index;

Household Income proxied by Gross Domestic Product (GDP);

Exchange rate between Australia and the source market;

Consumer sentiment proxied by Consumer Confidence Index (CCI);

Consumer price index (CPI)

Vector Auto regression model is used.

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Results

Selecting Proxy

Preparing Data

Tuning

Model

Parameters

Checking the results

Tourism Demand Forecast, of a Specific Market- (Average) Relative Error 0.56%

Tourism

Dem

and

($M

)

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Results

Selecting Proxy

Preparing Data

Tuning

Model

Parameters

Checking the results

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Results

Selecting Proxy

Preparing Data

Tuning

Model

Parameters

Checking the results

Impulse Response Analysis Is a good indicator of the behaviour of the model (system)

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Results

Selecting Proxy

Preparing Data

Tuning

Model

Parameters

Checking the results

Changes i

n T

ouris

m D

em

and due t

o 1

-std

change in

driv

ers

($’000)

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Results

Selecting Proxy

Preparing Data

Tuning

Model

Parameters

Checking the results

Indicator of How much of The dependent

variable is not explained by these

drivers

Changes i

n T

ouris

m D

em

and due t

o 1

-std

change in

driv

ers

($’000)

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Wealth (proxied by local stock exchange index), Exchange rate and income (proxiedby GDP) respectively are the key drivers of Tourism Demand.

The negative effects of changes in exchange rate on Tourism Demand appears faster than the positive effects of changes in wealth proxy.

Sensible strategy is to invest behind the trend. In similar situation for 2 markets of equal size, we should therefore invest more in the market with highest growth in the key drivers.

These Macro drivers strongly influence and predict Tourism demand. Rational strategy is to increase our market share;

Interpreting The Results

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The need for an analysis of the adequacy of model specifications and the

statistical deficiencies of existing empirical tourism demand models is sensed in this area.

To respond to this need, A framework for developing Econometric models

(using VAR) is suggested.

In this framework, to validate the model, We examine the impulse response

analysis of the structure of the model to see whether or not it matches the assumptions that we already have.

The results of our model using this platform, for tourism demand of a specific market was presented.

Conclusion