R. Joyeux and G. Milunovich – Forecasting Australian Passports Prepared for the 28 th Annual International Symposium on Forecasting Forecasting Demand for Australian Passports Dr. Roselyne Joyeux Department of Economics Macquarie University Dr. George Milunovich Department of Economics Macquarie University

R. Joyeux and G. Milunovich – Forecasting Australian Passports Prepared for the 28 th Annual International Symposium on Forecasting Outline of presentation Objectives Data Methods Forecast Evaluation Methodology Recommendation

R. Joyeux and G. Milunovich – Forecasting Australian Passports Prepared for the 28 th Annual International Symposium on Forecasting Objective Forecast demand for Australian passports: the number of passport applications lodged with the Department of Foreign Affairs 1. Aggregated Demand for Adult and Senior Citizens - 10 years & 5 years 2. Demand for Children’s (Minors) Passports – 5 year passports Forecasting horizons: 1. Short-term forecasts (forecast horizon < 1 yr.) 2. Medium to Long-term forecasts (> 1 yr.) Outcomes of this project currently assist agency planning and budgeting.

R. Joyeux and G. Milunovich – Forecasting Australian Passports Prepared for the 28 th Annual International Symposium on Forecasting Data Stochastic trends and other patterns in the number of passport applications make them difficult to forecast. Monthly FrequencyQuarterly Frequency

R. Joyeux and G. Milunovich – Forecasting Australian Passports Prepared for the 28 th Annual International Symposium on Forecasting Literature Forecasting passport demand: In the US: BearingPoint In Canada: Passports Canada Related Literature: Forecasting Tourism Demand Andrew, Cranage and Lee (1991); Carey (1991); Lim (1997); Morley (1993); Witt and Witt (1995), Wong (1997); Methods: Time-Series, Regression and ANN analyses Relevant variables: income, travelling cost, relative prices, exchange rates, population

R. Joyeux and G. Milunovich – Forecasting Australian Passports Prepared for the 28 th Annual International Symposium on Forecasting Forecasting Models Univariate models 1. ARIMA 2. ARIMAX - dynamic regression models Multivariate models 1. Vector Error Correction Models (VECM) models no exogenous variables 2. Vector Error Correction Models (VECM) models explanatory variables specified as exogenous Apply the models to: Levels Log Levels

R. Joyeux and G. Milunovich – Forecasting Australian Passports Prepared for the 28 th Annual International Symposium on Forecasting Identification/Model Selection Methodology Univariate models: Box-Jenkins (1976) Multivariate models – General to Specific (GETS) Hendry and Richard (1990) methodology that identifies a number of statistically significant explanatory variables from a given pool of potential candidates.

R. Joyeux and G. Milunovich – Forecasting Australian Passports Prepared for the 28 th Annual International Symposium on Forecasting Data Dataset includes three different types of variables: economic demographic chronological: “event” dummy variables Time Period: January 1987 – June 2007 Monthly data: 247 observations Quarterly data: 83 observations Trade-off between the: amount of information - monthly frequency number of explanatory variables - quarterly frequency

R. Joyeux and G. Milunovich – Forecasting Australian Passports Prepared for the 28 th Annual International Symposium on Forecasting Data: Monthly Frequency

R. Joyeux and G. Milunovich – Forecasting Australian Passports Prepared for the 28 th Annual International Symposium on Forecasting Data: Explanatory Variables Monthly Frequency

Data: Quarterly Frequency

R. Joyeux and G. Milunovich – Forecasting Australian Passports Prepared for the 28 th Annual International Symposium on Forecasting Data: Explanatory Variables

R. Joyeux and G. Milunovich – Forecasting Australian Passports Prepared for the 28 th Annual International Symposium on Forecasting Other Explanatory Variables 1. 28th of August 1986 adult passport validity switched from 5 years to 10 years. 2. September 2000: Sydney Olympics; 3. September 2001: Terrorist attacks; 4. October 12th 2002: Bali bombings; 5. April 2003: SARS epidemic; 6. December 2004: South East Asian Tsunami; 7. July 2005: London bombings; 8. August 2005: New Orleans floods in the U.S.

R. Joyeux and G. Milunovich – Forecasting Australian Passports Prepared for the 28 th Annual International Symposium on Forecasting Selected Variables – Joint Demand for Adult and Senior Passports Monthly Data: TWI All Ordinaries Share Price Index Dummy variable 1991:M2 Dummy Variable – beginning of the 1 st Gulf War 1997:M4 Dummy Variable – East Asian Financial Crisis 2003:M4 Dummy Variable – SARS epidemic, 2 nd Gulf War Seasonal Effects Quarterly Data TWI GDP 1991 – 1996 dummy variable Seasonal Effects

R. Joyeux and G. Milunovich – Forecasting Australian Passports Prepared for the 28 th Annual International Symposium on Forecasting Selected Variables – Demand for Children’s Passports Monthly Data: TWI All Ordinaries Share Price Index Population 1991:M10 Dummy Variable 2003:M4 Dummy Variable – SARS, 2 nd Gulf War Seasonal Effects Quarterly Data: TWI All Ordinaries – Share Price Index Spending House Prices Travel Costs Seasonal Effects

R. Joyeux and G. Milunovich – Forecasting Australian Passports Prepared for the 28 th Annual International Symposium on Forecasting Comparison of Forecasting Models We use 3 measures of forecast accuracy: Bias = Mean Absolute Error = Root Mean Squared Error =

R. Joyeux and G. Milunovich – Forecasting Australian Passports Prepared for the 28 th Annual International Symposium on Forecasting Forecast Horizon Models specified on the following sub-sample: 1987:M1 – 2004:M6 or 1987:Q1 – 2004:Q2 Models Evaluated on: 2004:M7 –2007:M6 or 2004:Q3 –2007:Q2 2005:M7 –2007:M6 or 2005:Q3 –2007:Q2 36 months or 12 quarters 24 months or 8 quarters

R. Joyeux and G. Milunovich – Forecasting Australian Passports Prepared for the 28 th Annual International Symposium on Forecasting Findings: Joint Demand for Adult and Senior Passports Monthly data Short run Forecasting - up to one year ARIMA models outperform the other models. Long run forecasting: from 1 to 3 years: VECM in log form with exogenous variables, VECM in log form with only endogenous variables, ARIMA and log ARIMA all perform well. from 2 to 3 years: VECM in log terms with exogenous variables performs best Quarterly data the quarterly models are outperformed by the aggregate forecasts obtained from the monthly models.

R. Joyeux and G. Milunovich – Forecasting Australian Passports Prepared for the 28 th Annual International Symposium on Forecasting Findings: Demand for Children’s Passports Monthly data Short run forecasting up to one year: ARIMA. ARIMAX, VECM log endogenous and VECM log exogenous all perform well. Long run forecasting: from 1 to 3 years: VECM in log form with exogenous variables, VECM in log form with only endogenous variables, ARIMA and log ARIMA all perform well. from 2 to 3 years: ARIMAX and log ARIMAX perform best, followed by the ARIMA and log ARIMA models. Quarterly data the quarterly models are outperformed by the aggregate forecasts obtained from the monthly models.

R. Joyeux and G. Milunovich – Forecasting Australian Passports Prepared for the 28 th Annual International Symposium on Forecasting Conclusions We construct a number of univariate and multivariate models with about the same degree of forecasting accuracy. Since multivariate models require inputs of certain macroeconomic variables, which are difficult to forecast (e.g. exchange rate), our final key recommendations are: Use the univariate ARIMA models for short to medium (i.e. 1-2 years) term forecasting. For longer term forecasts multivariate VECM models (without exogenous variables) should be preferred. Additionally, models constructed for monthly data outperform those formulated for quarterly data for the same forecasting horizons.

R. Joyeux and G. Milunovich – Forecasting Australian Passports Prepared for the 28 th Annual International Symposium on Forecasting Examples of forecasts

R. Joyeux and G. Milunovich – Forecasting Australian Passports Prepared for the 28 th Annual International Symposium on Forecasting