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

Forecasting Demand for Australian Passports

Dr. Roselyne Joyeux Department of Economics

Macquarie University

Dr. George MilunovichDepartment of Economics

Macquarie University

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

Outline of presentation

Objectives Data Methods Forecast Evaluation

Methodology Recommendation

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

Data

Stochastic trends and other patterns in the number of passport applications make them difficult to forecast.

Monthly Frequency Quarterly Frequency

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

Forecasting Models

Univariate models1. ARIMA2. ARIMAX - dynamic regression models

Multivariate models1. Vector Error Correction Models (VECM) models

no exogenous variables2. 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 PassportsPrepared for the 28th 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 PassportsPrepared for the 28th 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 PassportsPrepared for the 28th Annual International Symposium on Forecasting

Data: Monthly Frequency

Variable Available for PeriodAll Ordinaries Stock Market Index 1987:M1 - 2007:M6Spending 1987:M1 - 2007:M4Commodity Price Index (COM) 1987:M1 - 2007:M5USD/AUD Exchange Rate 1987:M1 - 2007:M6NZD/AUD Exchange Rate 1987:M1 - 2007:M6Trade Weighted Index 1987:M1 - 2007:M6Interest Rate 1987:M1 - 2007:M6Australian Population (population) 1987:M1 - 2007:M6Number of Passport Inquiries to the Call Centre – Calls 2000:M7 - 2007:M6Demand for Passports – Adults 1987:M1 - 2007:M6Demand for Passports – Minors 1987:M1 - 2007:M6Demand for Passports - Seniors 1987:M1 - 2007:M6

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

Data: Explanatory Variables Monthly Frequency

Data: Quarterly FrequencyVariable Available for Period

Net Migration (migration) 1987:Q1 - 2006:Q4Spending 1987:Q1 - 2007:Q1Nominal Disposable Income (NDI) 1987:Q1 - 2007:Q1Real Disposable Income (RDI) 1987:Q1 - 2007:Q1Consumer Price Index (CPI) 1987:Q1 - 2007:Q1Real Gross Domestic Product (RGDP) 1987:Q1 - 2007:Q1Nominal Gross Domestic Product (NGDP1) 1987:Q1 - 2007:Q1Real GDP per capita (RGDP_PC) 1987:Q1 - 2007:Q1GDP per capita (GDP_PC) 1987:Q1 - 2007:Q1Population 1987:Q1 - 2007:Q1Passport Demand - Adults 1987:Q1 - 2007:Q2Passport Demand - Minors 1987:Q1 - 2007:Q2Passport Demand - Seniors 1987:Q1 - 2007:Q2House Price Level (house) 1987:Q1 - 2007:Q1Travel Prices 1987:Q1 - 2006:Q4Earnings per capita 1987:Q1 - 2006:Q4All Ordinaries Stock Market Index (allords) 1987:Q1 - 2007:Q2USD/AUD Exchange Rate 1987:Q1 - 2007:Q1TWI 1987:Q1 - 2007:Q1Interest Rates 1987:Q1 - 2007:Q1Cost of Passports for Adults 1987:Q1 - 2007:Q2Cost of Passports for Minors 1987:Q1 - 2007:Q2

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

Data: Explanatory Variables

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

Selected Variables – Joint Demand for Adult and Senior Passports

Monthly Data: TWI All Ordinaries Share Price Index 1991 - 1996 Dummy variable 1991:M2 Dummy Variable – beginning of the 1st Gulf War 1997:M4 Dummy Variable – East Asian Financial Crisis 2003:M4 Dummy Variable – SARS epidemic, 2nd Gulf War Seasonal Effects

Quarterly Data TWI GDP 1991 – 1996 dummy variable Seasonal Effects

R. Joyeux and G. Milunovich – Forecasting Australian PassportsPrepared for the 28th 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, 2nd 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 PassportsPrepared for the 28th Annual International Symposium on Forecasting

Comparison of Forecasting Models We use 3 measures of forecast

accuracy:

Bias =

Mean Absolute Error =

Root Mean Squared Error =

1 , 1

1 ˆT

t t tt T n

V Vn

1 , 1

1 ˆT

t t tt T n

V Vn

2

1 , 1

1 ˆT

t t tt T n

V Vn

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

Findings: Joint Demand for Adult and Senior PassportsMonthly 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 PassportsPrepared for the 28th 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 PassportsPrepared for the 28th 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 PassportsPrepared for the 28th Annual International Symposium on Forecasting

Examples of forecastsLog Monthly Adults+Seniors

ARIMA model Forecasts from 2004:7

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

VECM – No Exogenous Monthly Log Adults+Seniors Forecasts from 2004:7

VECM (with exogenous) - Monthly Log Adults+Seniors - Forecasts from 2004:7

ARIMAX - Monthly Log Adults+Seniors - Forecasts from 2004:7