Modelling and Forecasting Australian Domestic Tourism George - PowerPoint PPT Presentation

Modelling and Forecasting Australian Domestic Tourism Modelling and Forecasting Australian Domestic Tourism George Athanasopoulos & Rob Hyndman

Modelling and Forecasting Australian Domestic Tourism Background Outline Background 1 Data 2 Regression models 3 Exponential smoothing via innovations 4 state space models Innovations state space models with 5 exogenous variables Forecasts 6 Conclusions and future research 7

Modelling and Forecasting Australian Domestic Tourism Background Australian Tourism Industry: International Arrivals 1 Outbound 2 Domestic Tourism 3

Modelling and Forecasting Australian Domestic Tourism Background Australian Tourism Industry: International Arrivals 1 Outbound 2 Domestic Tourism 3 $55 billion - more than 3 times international arrivals (TFC 2005)

Modelling and Forecasting Australian Domestic Tourism Background Australian Tourism Industry: International Arrivals 1 Outbound 2 Domestic Tourism 3 $55 billion - more than 3 times international arrivals (TFC 2005) Infrastructure maintenance

Modelling and Forecasting Australian Domestic Tourism Background Australian Tourism Industry: International Arrivals 1 Outbound 2 Domestic Tourism 3 $55 billion - more than 3 times international arrivals (TFC 2005) Infrastructure maintenance

Modelling and Forecasting Australian Domestic Tourism Background Australian Tourism Industry: International Arrivals 1 Outbound 2 Domestic Tourism 3 $55 billion - more than 3 times international arrivals (TFC 2005) Infrastructure maintenance My research - Research Fellow

Modelling and Forecasting Australian Domestic Tourism Background Australian Tourism Industry: International Arrivals 1 Outbound 2 Domestic Tourism 3 $55 billion - more than 3 times international arrivals (TFC 2005) Infrastructure maintenance My research - Research Fellow Tourism Australia STCRC Monash University

Modelling and Forecasting Australian Domestic Tourism Background Outline of presentation: Data 1 Regression framework 2 Exponential smoothing 3 Exp smoothing + Exogenous variables 4 Forecasts 5 Conclusions and Further research 6

Modelling and Forecasting Australian Domestic Tourism Data Outline Background 1 Data 2 Regression models 3 Exponential smoothing via innovations 4 state space models Innovations state space models with 5 exogenous variables Forecasts 6 Conclusions and future research 7

Modelling and Forecasting Australian Domestic Tourism Data National Visitor Survey - Visitor Nights (1998Q1-2005:Q2)

Modelling and Forecasting Australian Domestic Tourism Data National Visitor Survey - Visitor Nights (1998Q1-2005:Q2) Holiday VFR 30000 45000 28000 40000 26000 35000 24000 22000 30000 20000 1998 2000 2002 2004 1998 2000 2002 2004 Business Other 7000 13000 6000 12000 5000 11000 4000 10000 3000 9000 1998 2000 2002 2004 1998 2000 2002 2004

Modelling and Forecasting Australian Domestic Tourism Data Aggregate Data & TFC Forecasts: VN Sample TFC forecasts 90000 80000 70000 60000 2000 2005 2010 2015

Modelling and Forecasting Australian Domestic Tourism Regression models Outline Background 1 Data 2 Regression models 3 Exponential smoothing via innovations 4 state space models Innovations state space models with 5 exogenous variables Forecasts 6 Conclusions and future research 7

Modelling and Forecasting Australian Domestic Tourism Regression models Tourism demand function: VN i = f ( t , DEBT t , DPI t , GDP t , BALI t , OLYMP t , MAR t , JUN t , SEP t , ε t ) t

Modelling and Forecasting Australian Domestic Tourism Regression models Tourism demand function: VN i = f ( t , DEBT t , DPI t , GDP t , BALI t , OLYMP t , MAR t , JUN t , SEP t , ε t ) t VN i t - ln(Visitor nights per capita travelling for purpose i )

Modelling and Forecasting Australian Domestic Tourism Regression models Tourism demand function: VN i = f ( t , DEBT t , DPI t , GDP t , BALI t , OLYMP t , MAR t , JUN t , SEP t , ε t ) t VN i t - ln(Visitor nights per capita travelling for purpose i ) t - exponential trend

Modelling and Forecasting Australian Domestic Tourism Regression models Tourism demand function: VN i = f ( t , DEBT t , DPI t , GDP t , BALI t , OLYMP t , MAR t , JUN t , SEP t , ε t ) t VN i t - ln(Visitor nights per capita travelling for purpose i ) t - exponential trend DEBT t - Growth rate of real personal debt per capita

Modelling and Forecasting Australian Domestic Tourism Regression models Tourism demand function: VN i = f ( t , DEBT t , DPI t , GDP t , BALI t , OLYMP t , MAR t , JUN t , SEP t , ε t ) t VN i t - ln(Visitor nights per capita travelling for purpose i ) t - exponential trend DEBT t - Growth rate of real personal debt per capita DPI t - Growth rate of domestic price index

Modelling and Forecasting Australian Domestic Tourism Regression models Tourism demand function: VN i = f ( t , DEBT t , DPI t , GDP t , BALI t , OLYMP t , MAR t , JUN t , SEP t , ε t ) t VN i t - ln(Visitor nights per capita travelling for purpose i ) t - exponential trend DEBT t - Growth rate of real personal debt per capita DPI t - Growth rate of domestic price index GDP t - Growth rate of real GDP per capita

Modelling and Forecasting Australian Domestic Tourism Regression models Tourism demand function: VN i = f ( t , DEBT t , DPI t , GDP t , BALI t , OLYMP t , MAR t , JUN t , SEP t , ε t ) t VN i t - ln(Visitor nights per capita travelling for purpose i ) t - exponential trend DEBT t - Growth rate of real personal debt per capita DPI t - Growth rate of domestic price index GDP t - Growth rate of real GDP per capita BALI t - 1 for 2002:Q4 and beyond, 0 otherwise

Modelling and Forecasting Australian Domestic Tourism Regression models Tourism demand function: VN i = f ( t , DEBT t , DPI t , GDP t , BALI t , OLYMP t , MAR t , JUN t , SEP t , ε t ) t VN i t - ln(Visitor nights per capita travelling for purpose i ) t - exponential trend DEBT t - Growth rate of real personal debt per capita DPI t - Growth rate of domestic price index GDP t - Growth rate of real GDP per capita BALI t - 1 for 2002:Q4 and beyond, 0 otherwise OLYMP t - 1 for 2000:Q4, 0 otherwise

Modelling and Forecasting Australian Domestic Tourism Regression models Tourism demand function: VN i = f ( t , DEBT t , DPI t , GDP t , BALI t , OLYMP t , MAR t , JUN t , SEP t , ε t ) t VN i t - ln(Visitor nights per capita travelling for purpose i ) t - exponential trend DEBT t - Growth rate of real personal debt per capita DPI t - Growth rate of domestic price index GDP t - Growth rate of real GDP per capita BALI t - 1 for 2002:Q4 and beyond, 0 otherwise OLYMP t - 1 for 2000:Q4, 0 otherwise MAR t , JUN t , SEP t - Seasonal dummies

Modelling and Forecasting Australian Domestic Tourism Regression models Tourism demand function: VN i = f ( t , DEBT t , DPI t , GDP t , BALI t , OLYMP t , MAR t , JUN t , SEP t , ε t ) t VN i t - ln(Visitor nights per capita travelling for purpose i ) t - exponential trend DEBT t - Growth rate of real personal debt per capita DPI t - Growth rate of domestic price index GDP t - Growth rate of real GDP per capita BALI t - 1 for 2002:Q4 and beyond, 0 otherwise OLYMP t - 1 for 2000:Q4, 0 otherwise MAR t , JUN t , SEP t - Seasonal dummies ε t - random error term

Modelling and Forecasting Australian Domestic Tourism Regression models Tourism demand function: VN i = f ( t , DEBT t , DPI t , GDP t , BALI t , OLYMP t , MAR t , JUN t , SEP t , ε t ) t VN i t - ln(Visitor nights per capita travelling for purpose i ) t - exponential trend DEBT t - Growth rate of real personal debt per capita DPI t - Growth rate of domestic price index GDP t - Growth rate of real GDP per capita BALI t - 1 for 2002:Q4 and beyond, 0 otherwise OLYMP t - 1 for 2000:Q4, 0 otherwise MAR t , JUN t , SEP t - Seasonal dummies ε t - random error term

Modelling and Forecasting Australian Domestic Tourism Regression models Tourism demand function: VN i = f ( t , DEBT t , DPI t , GDP t , BALI t , OLYMP t , MAR t , JUN t , SEP t , ε t ) t VN i t - ln(Visitor nights per capita travelling for purpose i ) t - exponential trend DEBT t - Growth rate of real personal debt per capita DPI t - Growth rate of domestic price index GDP t - Growth rate of real GDP per capita BALI t - 1 for 2002:Q4 and beyond, 0 otherwise OLYMP t - 1 for 2000:Q4, 0 otherwise MAR t , JUN t , SEP t - Seasonal dummies ε t - random error term Step 1: Run OLS and test for upto 1 lag of each variable.

Modelling and Forecasting Australian Domestic Tourism Regression models Tourism demand function: VN i = f ( t , DEBT t , DPI t , GDP t , BALI t , OLYMP t , MAR t , JUN t , SEP t , ε t ) t VN i t - ln(Visitor nights per capita travelling for purpose i ) t - exponential trend DEBT t - Growth rate of real personal debt per capita DPI t - Growth rate of domestic price index GDP t - Growth rate of real GDP per capita BALI t - 1 for 2002:Q4 and beyond, 0 otherwise OLYMP t - 1 for 2000:Q4, 0 otherwise MAR t , JUN t , SEP t - Seasonal dummies ε t - random error term Step 1: Run OLS and test for upto 1 lag of each variable. Step 2: Sequentially drop insignificant parameters and estimate efficiently using SUR.