Forecasting air passenger numbers with a GVAR model

Abstract This study employs a GVAR model on the passenger numbers of the top 20 busiest airports of the world and the Asia-Pacific and Latin America-Caribbean regions. With air passenger numbers representing a demand measure, country-level proxies for economic drivers are included as domestic and foreign variables. In terms of ex-ante forecast accuracy, the GVAR model performs best for several airports – yet not for the entirety of airports – compared to four benchmarks for horizons one and three quarters ahead. It also achieves several second and third ranks for these and two other horizons and when all horizons are evaluated jointly. Considering the connectivity of airports is worthwhile to achieve accurate and economically interpretable air passenger demand forecasts.

[1]  Mark P. Taylor,et al.  What if the UK or Sweden had joined the Euro in 1999? An empirical evaluation using a global VAR , 2007 .

[2]  A. Gilbey,et al.  Forecasting of Hong Kong airport's passenger throughput , 2014 .

[3]  Bernhard Pfaff,et al.  VAR, SVAR and SVEC Models: Implementation Within R Package vars , 2008 .

[4]  Rodrigo Arnaldo Scarpel,et al.  A demand trend change early warning forecast model for the city of São Paulo multi-airport system , 2014 .

[5]  Haiyan Song,et al.  Forecasting tourism recovery amid COVID-19 , 2021, Annals of Tourism Research.

[6]  Vera Shanshan Lin,et al.  Ex Ante Tourism Forecasting Assessment , 2020, Journal of Travel Research.

[7]  Katrin Kölker,et al.  Approach to Forecast Air Traffic Movements at Capacity-Constrained Airports , 2014 .

[8]  Emmanuel Sirimal Silva,et al.  Forecasting Accuracy Evaluation of Tourist Arrivals: Evidence from Parametric and Non-Parametric Techniques , 2015 .

[9]  Søren Johansen,et al.  Cointegration in partial systems and the efficiency of single-equation analysis , 1992 .

[10]  Gang Li,et al.  Tourism forecasting research: a perspective article , 2020 .

[11]  Hugo M. Repolho,et al.  Air transportation demand forecast through Bagging Holt Winters methods , 2017 .

[12]  Robert Fildes,et al.  Evaluating the forecasting performance of econometric models of air passenger traffic flows using multiple error measures , 2011 .

[13]  Haiyan Song,et al.  Air Travel Demand Studies: A Review , 2010 .

[14]  Peter Berster,et al.  A new direct demand model of long-term forecasting air passengers and air transport movements at German airports , 2018 .

[15]  J. M. Seixas,et al.  A multivariate neural forecasting modeling for air transport – Preprocessed by decomposition: A Brazilian application , 2009 .

[16]  P. Cashin,et al.  The Global Impact of the Systemic Economies and MENA Business Cycles , 2012, SSRN Electronic Journal.

[17]  M. Pesaran,et al.  Exploring the International Linkages of the Euro Area: A Global VAR Analysis , 2006, SSRN Electronic Journal.

[18]  Shaun S. Wulff,et al.  Time Series Analysis: Forecasting and Control, 5th edition , 2017 .

[19]  Jun Zhang,et al.  Evolution of Chinese airport network , 2010, Physica A: Statistical Mechanics and its Applications.

[20]  Forecasting passenger movement for Brazilian airports network based on the segregation of primary and secondary demand applied to Brazilian civil aviation policies planning , 2019, Transport Policy.

[21]  Rob Law,et al.  Tourism demand forecasting: A deep learning approach , 2019, Annals of Tourism Research.

[22]  E. Smeral,et al.  Scientific value of econometric tourism demand studies , 2019, Annals of Tourism Research.

[23]  Marcos Álvarez-Díaz,et al.  Forecasting International Tourism Demand Using a Non-Linear Autoregressive Neural Network and Genetic Programming , 2018, Forecasting.

[24]  Hongtao Li,et al.  Forecasting air passenger demand with a new hybrid ensemble approach , 2020 .

[25]  M. Pesaran,et al.  Constructing Multi‐Country Rational Expectations Models , 2014 .

[26]  David A. Hensher.  Determining passenger potential for a regional airline hub at Canberra International Airport , 2002 .

[27]  Gang Li,et al.  Forecasting Seasonal Tourism Demand Using a Multiseries Structural Time Series Method , 2019 .

[28]  Seongdo Kim,et al.  Forecasting short-term air passenger demand using big data from search engine queries , 2016 .

[29]  Rob J Hyndman,et al.  Forecasting with Exponential Smoothing: The State Space Approach , 2008 .

[30]  Determinants of air travel demand: The role of low-cost carriers, ethnic links and aviation-dependent employment , 2018, Transportation Research Part A: Policy and Practice.

[31]  C. Sims.  MACROECONOMICS AND REALITY , 1977 .

[32]  P. Phillips,et al.  Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? , 1992 .

[33]  Michael McAleer,et al.  An alternative approach to estimating demand: neural network regression with conditional volatility for high frequency air passenger arrivals. , 2008 .

[34]  J. Cidell.  Air Transportation, Airports, and the Discourses and Practices of Globalization , 2006 .

[35]  V. Profillidis,et al.  An ex-post assessment of a passenger demand forecast of an airport , 2012 .

[36]  S. Mas‐Coma,et al.  COVID-19 and globalization , 2020, One Health.

[37]  M. Bussière,et al.  Modelling Global Trade Flows: Results from a GVAR Model , 2009, SSRN Electronic Journal.

[38]  Haiyan Song,et al.  New developments in tourism and hotel demand modeling and forecasting , 2017 .

[39]  H. Akaike.  A new look at the statistical model identification , 1974 .

[40]  Haiyan Song,et al.  A review of research on tourism demand forecasting: Launching the Annals of Tourism Research Curated Collection on tourism demand forecasting , 2019, Annals of Tourism Research.

[41]  Haiyan Song,et al.  The Advanced Econometrics of Tourism Demand , 2008 .

[42]  Megan S. Ryerson,et al.  Forecast to grow: Aviation demand forecasting in an era of demand uncertainty and optimism bias , 2019, Transportation Research Part E: Logistics and Transportation Review.

[43]  Víctor Leiva,et al.  An R Package for a General Class of Inverse Gaussian Distributions , 2008 .

[44]  Jason Li Chen,et al.  Tourism forecasting: A review of methodological developments over the last decade , 2018, Tourism Economics.

[45]  Vassilios A. Profillidis,et al.  Econometric and fuzzy models for the forecast of demand in the airport of Rhodes , 2000 .

[46]  Dimitriou J. Dimitrios,et al.  Quantification of the air transport industry socio-economic impact on regions heavily depended on tourism , 2017 .

[47]  Gang Li,et al.  Forecasting tourism demand with multisource big data , 2020 .

[48]  Chris Chatfield,et al.  Time‐series forecasting , 2000 .

[49]  Antony Evans,et al.  The impact of airport capacity constraints on future growth in the US air transportation system , 2011 .

[50]  P. Michaelides,et al.  Tourism expenditures and crisis transmission: A general equilibrium GVAR analysis with network theory , 2017 .

[51]  Mike G. Tsionas,et al.  Modeling and Forecasting Regional Tourism Demand Using the Bayesian Global Vector Autoregressive (BGVAR) Model , 2019 .

[52]  M. Pesaran,et al.  Modeling Regional Interdependencies Using a Global Error-Correcting Macroeconometric Model , 2004 .

[53]  Joseph M. Sussman,et al.  The impact of high-speed rail and low-cost carriers on European air passenger traffic , 2014 .

[54]  Rob J Hyndman,et al.  Automatic Time Series Forecasting: The forecast Package for R , 2008 .

[55]  Rob J Hyndman,et al.  A state space framework for automatic forecasting using exponential smoothing methods , 2002 .

[56]  Muhammad Yousaf Shad,et al.  The impact of COVID-19 on globalization , 2020, One Health.

[57]  Gang Li,et al.  Tourism Demand Modelling and Forecasting: How Should Demand Be Measured? , 2010 .

[58]  M. Wolters,et al.  Comparative analysis of government forecasts for the Lisbon Airport , 2010 .

[59]  F. Diebold,et al.  Comparing Predictive Accuracy , 1994, Business Cycles.

[60]  Paul Newbold,et al.  Testing the equality of prediction mean squared errors , 1997 .

[61]  E. Fernandes,et al.  Air transport demand and economic growth in Brazil: A time series analysis , 2010 .

[62]  Rodrigo Arnaldo Scarpel,et al.  Forecasting air passengers at São Paulo International Airport using a mixture of local experts model , 2013 .

[63]  Cynthia Barnhart,et al.  Modeling Passenger Travel and Delays in the National Air Transportation System , 2014, Oper. Res..

[64]  Gang Li,et al.  Modelling the interdependence of tourism demand: The global vector autoregressive approach , 2017 .

[65]  R. Law,et al.  The Methodological Progress of Tourism Demand Forecasting: A Review of Related Literature , 2011 .

[66]  Emmanuel Sirimal Silva,et al.  Forecasting tourism demand with denoised neural networks , 2019, Annals of Tourism Research.

[67]  B. Derudder,et al.  Airline connectivity as a measure of the globalization of African cities , 2011 .

[68]  Florian Huber,et al.  Forecasting with Global Vector Autoregressive Models: a Bayesian Approach , 2016 .

[69]  Kin Keung Lai,et al.  A neuro-fuzzy combination model based on singular spectrum analysis for air transport demand forecasting , 2014 .

[70]  Mark Hansen,et al.  An aggregate demand model for air passenger traffic in the hub-and-spoke network , 2006 .

[71]  Shuo-Yan Chou,et al.  Air passenger demand forecasting and passenger terminal capacity expansion: A system dynamics framework , 2010, Expert Syst. Appl..

[72]  Dimitrios J. Dimitriou,et al.  Social Dimension of Air Transport Sustainable Development , 2018 .

[73]  Haiyan Song,et al.  Tourism demand modelling and forecasting—A review of recent research , 2008 .

[74]  Desa.  Role of the International Recommendations for Tourism Statistics 2008 , 2017 .

[75]  S. Johansen,et al.  Asymptotic Inference on Cointegrating Rank in Partial Systems , 1998 .

[76]  Kin Keung Lai,et al.  Short-term forecasting of air passenger by using hybrid seasonal decomposition and least squares support vector regression approaches , 2014 .

[77]  Douglas C. Frechtling,et al.  Forecasting Tourism Demand: Methods and Strategies , 2001 .

[78]  Kuo-Wei Chang,et al.  Improving the forecasting accuracy of air passenger and air cargo demand: the application of back-propagation neural networks , 2012 .

[79]  Tolga Cenesizoglu,et al.  Forecasting (Aggregate) Demand for U.S. Commercial Air Travel , 2010 .

[80]  Leonidas S. Rompolis,et al.  Forecasting the mean and volatility of stock returns from option prices , 2006 .

[81]  Ulrich Gunter.  Conditional forecasts of tourism exports and tourism export prices of the EU-15 within a global vector autoregression framework , 2017 .

[82]  Sue Ling Lai,et al.  Impact analysis of September 11 on air travel demand in the USA , 2005 .

[83]  Haiyan Song,et al.  Recent Developments in Econometric Modeling and Forecasting , 2005 .