Air traffic forecast in post-liberalization context: a dynamic linear models approach

dc.contributor.authorRODRIGUEZ, Yesid.spa
dc.contributor.authorPINEDA, Wilmer.spa
dc.contributor.authorDIAZ OLARIAGA, Oscar.spa
dc.contributor.cvlachttp://scienti.colciencias.gov.co:8081/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001561684spa
dc.contributor.cvlachttp://scienti.colciencias.gov.co:8081/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001256491spa
dc.contributor.cvlachttp://scienti.colciencias.gov.co:8081/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001454199spa
dc.contributor.googlescholarhttps://scholar.google.com/citations?user=9gC738EAAAAJ&hl=esspa
dc.contributor.googlescholarhttps://scholar.google.es/citations?user=5KmOl5oAAAAJ&hl=esspa
dc.contributor.googlescholarhttps://scholar.google.com/citations?user=v4XBXJAAAAAJ&hl=esspa
dc.contributor.orcidhttps://orcid.org/0000-0002-9553-0455spa
dc.contributor.orcidhttps://orcid.org/0000-0001-7774-951Xspa
dc.contributor.orcidhttps://orcid.org/0000-0002-4858-3677spa
dc.coverage.campusCRAI-USTA Bogotáspa
dc.date.accessioned2020-05-28T20:01:48Zspa
dc.date.available2020-05-28T20:01:48Zspa
dc.date.issued2020-05-28spa
dc.description.abstractThe process of air transport liberalization in Colombia began in 1991. Liberalization entailed the entry of private capital into the airport sector which subsequently led, in several temporary phases, to the privatization of the country’s main airports. Simultaneously, new air operators entered the market. This new market situation, supported by the complete deregulation of airfares, generated a dynamic and sustained growth of air transport in Colombia for two decades. Within the context of post-liberalization, this article presents a forecast (medium-term – 5 years period) of air traffic in the country’s main airport using DLMs (Dynamic Linear Models). It has the following advantages vs. the usual forecast calculation methodologies: it detects stochastic tendencies that are hidden in the time series. It also detects structural changes that allow estimating the variable effect of exogenous shocks over time without increasing the number of parameters. From the results obtained, it should be noted that the application of DLMs presents MAPE (Mean Absolute Percentage Error) values below 1%, which guarantees predictions of higher accuracy and thus introduces a new alternative model to develop reliable forecasts in air transport, at least in the medium-term.spa
dc.description.domainhttp://unidadinvestigacion.usta.edu.cospa
dc.format.mimetypeapplication/pdfspa
dc.identifier.citationRodriguez, Y., Pineda, W., & Diaz Olariaga, O. (2020). Air traffic forecast in post-liberalization context: a Dynamic Linear Models approach. Aviation, 24(1), 10-19. https://doi.org/10.3846/aviation.2020.12273spa
dc.identifier.doihttps://doi.org/10.3846/aviation.2020.12273spa
dc.identifier.urihttp://hdl.handle.net/11634/23520
dc.relation.referencesAbate, M.A. (2016). Economic effects of air transport market liberalization in Africa. Transportation Research Part A, 92, 326–337. https://doi.org/10.1016/j.tra.2016.06.014spa
dc.relation.referencesAbed, S. Y., Ba-Fail, A. O., & Jasimuddin, S. M. (2001). An econometric analysis of international air travel demand in Saudi Arabia. Journal of Air Transport Management, 7, 143– 148. https://doi.org/10.1016/S0969-6997(00)00043-0spa
dc.relation.referencesACI. (2016). Guide to World Airport Traffic Forecasts. Montreal: Airports Council International.spa
dc.relation.referencesAerocivil. (2019). Statistics. http://www.aerocivil.gov.co/atencion/estadisticas-de-las-actividades-aeronauticasspa
dc.relation.referencesAhn, S., & Schmidt, P. (1995). Efficient estimation of models for dynamic panel data. Journal of Econometrics, 68, 5–27. https://doi.org/10.1016/0304-4076(94)01641-Cspa
dc.relation.referencesArellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277–297. https://doi.org/10.2307/2297968spa
dc.relation.referencesArellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of Error-Components Models. Journal of Econometrics, 68, 29–51. https://doi.org/10.1016/0304-4076(94)01642-Dspa
dc.relation.referencesAsparouhov, T., Hamaker, E., & Muthén, B. (2018). Dynamic structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 25(3), 359–388. https://doi.org/10.1080/10705511.2017.1406803spa
dc.relation.referencesAston, J. A. D. & Koopman, S. J. (2006). A non-Gaussian generalization of the airline model for robust seasonal adjustment. Journal of Forecasting, 25, 325–349. https://doi.org/10.1002/for.991spa
dc.relation.referencesBanco de la República de Colombia. (2019). Estadísticas. http:// www.banrep.gov.co/es/-estadisticasspa
dc.relation.referencesBolstad, W. (2007). Introduction to Bayesian statistics. Wiley. https://doi.org/10.1002/9780470181188spa
dc.relation.referencesBox, G., Jenkins, G., Reinsel, G., & Ljung, G. (2016). Time series analysis: forecasting and control. John Wiley & Sons.spa
dc.relation.referencesBowen, J. (2002). Network change, deregulation, and access in the global airline industry. Economic Geography, 78(4), 425– 439. https://doi.org/10.2307/4140797spa
dc.relation.referencesBowen, J. (2000). Airline hubs in Southeast Asia: national economic development and nodal accessibility. Journal of Transport Geography, 8(1), 25–41. https://doi.org/10.1016/S0966-6923(99)00030-7spa
dc.relation.referencesBowen, J., & Leinbach, T. (1995). The state and liberalization: the airline industry in the East Asian NICs. Annals of the Association of American Geographers, 85(3), 468–493. https://doi.org/10.1111/j.1467-8306.1995.tb01809.xspa
dc.relation.referencesBrooks, C. (2008). Introductory econometrics for finance. Cambridge (UK): Cambridge University Press. https://doi.org/10.1017/CBO9780511841644spa
dc.relation.referencesChin, A. T. H., & Tay, J. H. (2001). Developments in air transport: implications on investment decisions, profitability and survival of Asian airlines. Journal of Air Transport Management, 7, 319–330. https://doi.org/10.1016/S0969-6997(01)00026-6spa
dc.relation.referencesChou, Y. H. (1993). Airline deregulation and nodal accessibility. Journal of Transport Geography, 1(1), 36–46. https://doi.org/10.1016/0966-6923(93)90036-Yspa
dc.relation.referencesDANE – Departamento Administrativo Nacional de Estadística. (2019). https://www.dane.gov.co/index.php/estadisticas-por-temaspa
dc.relation.referencesDantas, T., Oliveira, F., & Repolho, H. (2017). Air transportation demand forecast through Bagging Holt Winters methods. Journal of Air Transport Management, 59, 116–123. https://doi.org/10.1016/j.jairtraman.2016.12.006spa
dc.relation.referencesDaramola, A., & Jaja, C. (2011). Liberalization and changing spatial configurations in Nigeria’s domestic air transport network. Journal of Transport Geography, 19, 1198–1209. https://doi.org/10.1016/j.jtrangeo.2011.05.008spa
dc.relation.referencesde Neufville, R., & Odoni, A. (2013). Airport systems, planning, design, and management. McGrawHill.spa
dc.relation.referencesDebbage, K. (1993). U.S. airport market concentration and deconcentration. Transportation Journal, 47(1), 115–136.spa
dc.relation.referencesDennis, N. (1994). Airline hub operations in Europe. Journal of Transport Geography, 2(4), 219–223. https://doi.org/10.1016/0966-6923(94)90047-7spa
dc.relation.referencesDerudder, B., & Witlox, F. (2009). The impact of progressive liberalization on the spatiality of airline networks: a measurement framework based on the assessment of hierarchical differentiation. Journal of Transport Geography, 17, 276–284. https://doi.org/10.1016/j.jtrangeo.2009.02.001spa
dc.relation.referencesDíaz Olariaga, O., & Zea, J. F. (2018). Influence of the liberalization of the air transport industry on configuration of the traffic in the airport network. Transportation Research Procedia, 33, 43–50. https://doi.org/10.1016/j.trpro.2018.10.074spa
dc.relation.referencesDíaz Olariaga, O. (2017). Políticas de privatización de aeropuertos. El caso de Colombia. Documentos y Aportes en Administración Pública y Gestión Estatal, 29, 7–35. https://doi.org/10.1016/j.trpro.2018.10.074spa
dc.relation.referencesDíaz Olariaga, O., Girón Amaya, E., & Mora-Camino, F. (2017, 10–12 octubre). Pronóstico de la demanda de pasajeros en aeropuertos privatizados. VI Congreso Internacional de la Red Iberoamericana de Investigación en Transporte Aéreo. Santiago de Chile.spa
dc.relation.referencesDíaz Olariaga, O., & Carvajal, A. F. (2016). Efectos de la liberalización en la geografía del transporte aéreo en Colombia. Cuadernos Geográficos, 55(2), 344–364.spa
dc.relation.referencesDíaz Olariaga, O., & Ávila, J. (2015). Evolution of the airport and air transport industry in Colombia and its impact on the economy. Journal of Airline and Airport Management, 5(1), 39–66. https://doi.org/10.3926/jairm.43spa
dc.relation.referencesDobruszkes, F., Mondou, V., & Ghedira, A. (2016). Assessing the impacts of aviation liberalisation on tourism: some methodological considerations derived from the Moroccan and Tunisian cases. Journal of Transport Geography, 50, 115–127. https://doi.org/10.1016/j.jtrangeo.2015.06.022spa
dc.relation.referencesDobruszkes, F. (2009). Does liberalisation of air transport imply increasing competition? Lessons from the European case. Transport Policy, 16, 29–39. https://doi.org/10.1016/j.tranpol.2009.02.007spa
dc.relation.referencesDurbin, J. & Koopman, S. (2012). Time series analysis by state space methods. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199641178.001.0001spa
dc.relation.referencesEriksson, M., & Pettersson, T. (2012). Adapting to liberalization: government procurement of interregional passenger transports in Sweden, 1989–2008. Journal of Transport Geography, 24, 182–188. https://doi.org/10.1016/j.jtrangeo.2012.02.001spa
dc.relation.referencesFan, T. (2006). Improvements in intra-Europe inter-city flight connectivity, 1996–2004. Journal of Transport Geography, 14(4), 273–286. https://doi.org/10.1016/j.jtrangeo.2005.08.006spa
dc.relation.referencesFan, T., Vigeant-Langlois, L., Geissler, C., Bosler, B., & Wilmaking, J. (2001). Evolution of global airline strategic alliance and consolidation in the twenty-first century. Journal of Air Transport Management, 7(6), 349–360. https://doi.org/10.1016/S0969-6997(01)00027-8spa
dc.relation.referencesFernandes, E., & Pacheco, R. R. (2010). The causal relationship between GDP and domestic air passenger traffic in Brazil. Transportation Planning and Technology, 33, 569–581. https://doi.org/10.1080/03081060.2010.512217spa
dc.relation.referencesForsyth, P. (1991). The regulation and deregulation of Australia’s domestic airline industry. In K. Button (Ed.), Airline deregulation: international experiences (pp. 48–84). David Fulton Publishers, London. https://doi.org/10.4324/9781315212036-3spa
dc.relation.referencesGarrow, L. A., & Koppelman, F. S. (2004). Predicting air travelers’ no-show and standby behavior using passenger and directional itinerary information. Journal of Air Transport Management, 10, 401–411. https://doi.org/10.1016/j.jairtraman.2004.06.007spa
dc.relation.referencesGelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian data analysis (3rd ed.). Chapman & Hall/CRC Press. https://doi.org/10.1201/b16018spa
dc.relation.referencesGlynn, C., Tokdar, S. T., Howard, B., & Banks, D. L. (2019). Bayesian analysis of dynamic linear topic models. Bayesian Analysis, 14(1), 53–80. https://doi.org/10.1214/18-BA1100spa
dc.relation.referencesGoetz, A. & Graham, B. (2004). Air transport globalization, liberalization and sustainability: post-2001 policy dynamics in the United States and Europe. Journal of Transport Geography, 12(4), 265–276. https://doi.org/10.1016/j.jtrangeo.2004.08.007spa
dc.relation.referencesGoetz, A. (2002). Deregulation, competition, and antitrust implications in the US airline industry. Journal of Transport Geography, 10(1), 1–19. https://doi.org/10.1016/S0966-6923(01)00034-5spa
dc.relation.referencesGoetz, A., & Sutton, C. (1998). The geography of deregulation in the U.S. airline industry. Annals of the Association of American Geographers, 87(2), 238–263. https://doi.org/10.1111/0004-5608.872052spa
dc.relation.referencesGraham, B. (1998). Liberalization, regional economic development and the geography of demand for air transport in the European Union. Journal of Transport Geography, 6(2), 87– 104. https://doi.org/10.1016/S0966-6923(98)00003-9spa
dc.relation.referencesGraham, B. (1997). Regional airline services in the liberalized European Union single aviation market. Journal of Air Transport Management, 3(4), 227–238. https://doi.org/10.1016/S0969-6997(97)00032-Xspa
dc.relation.referencesGraham, B. (1993). The regulation of deregulation: a comment on the liberalization of the U.K’.s scheduled airline industry. Journal of Transport Geography, 1(2), 125–131. https://doi.org/10.1016/0966-6923(93)90006-Lspa
dc.relation.referencesGrosche, T., Rothlauf, F., & Heinzl, A. (2007). Gravity models for airline passenger volume estimation. Journal of Air Transport Management, 13, 175–183. https://doi.org/10.1016/j.jairtraman.2007.02.001spa
dc.relation.referencesHalpern, N. (2011). Measuring seasonal demand for Spanish airports: implications for counter-seasonal marketing. Research in Transportation Business & Management, 1(1), 47–54. https://doi.org/10.1016/j.rtbm.2011.05.005spa
dc.relation.referencesHonjo, K.; Shiraki, H., & Ashina, S. (2018). Dynamic linear modeling of monthly electricity demand in Japan: Time variation of electricity conservation effect. PloS ONE, 13(4), e0196331. https://doi.org/10.1371/journal.pone.0196331spa
dc.relation.referencesHooper, P. (1998). Airline competition and deregulation in developed and developing country contexts – Australia and India. Journal of Transport Geography, 6(2), 105–116. https://doi.org/10.1016/S0966-6923(98)00004-0spa
dc.relation.referencesHoronjeff, R., McKelvey, F., Sproule, W., & Young, S. (2010). Planning and design of airports. McGrawHill.spa
dc.relation.referencesICAO. (2006). Manual of air traffic forecasting. Montreal: ICAO.spa
dc.relation.referencesIsmaila, D. A. I., Warnock-Smith, D., & Hubbard, N. (2014). The impact of air service agreement liberalisation: the case of Nigeria. Journal of Air Transport Managament, 37, 69–75. https://doi.org/10.1016/j.jairtraman.2014.02.001spa
dc.relation.referencesIvy, R. (1995). The restructuring of air transport linkages in the new Europe. Professional Geographer, 47(3), 280–288. https://doi.org/10.1111/j.0033-0124.1995.00280.xspa
dc.relation.referencesJin, F., Li, Y., Sun, S., & Li, H. (2020). Forecasting air passenger demand with a new hybrid ensemble approach. Journal of Air Transport Management, 83, 1–18. https://doi.org/10.1016/j.jairtraman.2019.101744spa
dc.relation.referencesJankiewicz, J., & Huderek-Glapska, S. (2015). The air transport market in Central and Eastern Europe after a decade of liberalisation – Different paths of growth. Journal of Transport Geography, 50, 45–56. https://doi.org/10.1016/j.jtrangeo.2015.06.002spa
dc.relation.referencesKazda, A., & Caves, R. (2015). Airport design and operations. Emerald.spa
dc.relation.referencesKenkel, J. (2018). Dynamic linear economic models. London: Routledge. https://doi.org/10.4324/9781351140720spa
dc.relation.referencesKim, S., & Kim, H. (2016). A new metric of absolute percentage error for intermittent demand forecasts. International Journal of Forecasting, 32(3), 669–679. https://doi.org/10.1016/j.ijforecast.2015.12.003spa
dc.relation.referencesKoo, T., & Lohmann, G. (2013). The spatial effects of domestic aviation deregulation: a comparative study of Australian and Brazilian seat capacity, 1986–2010. Journal of Transport Geography, 29, 52–62. https://doi.org/10.1016/j.jtrangeo.2012.12.011spa
dc.relation.referencesKoo, T., Tan, S., & Duval, D. (2013). Direct air transport and demand interaction: A vector error-correction model approach. Journal of Air Transport Management, 28, 14–19. https://doi.org/10.1016/j.jairtraman.2012.12.005spa
dc.relation.referencesLaine, M. (2019). Introduction to dynamic linear models for time series analysis. arXiv:1903.11309v2 [stat.ME], 21 May 2019.spa
dc.relation.referencesMcAlinn, K., & West, M. (2019). Dynamic Bayesian predictive synthesis in time series forecasting. Journal of Econometrics, 210(1), 155–169. https://doi.org/10.1016/j.jeconom.2018.11.010spa
dc.relation.referencesNjoya, E., Christidis, P., & Nikitas, A. (2018). Understanding the impact of liberalisation in the EU-Africa aviation market. Journal of Transport Geography, 71, 161–171. https://doi.org/10.1016/j.jtrangeo.2018.07.014spa
dc.relation.referencesNjoya, E. T. (2015). Africa’s single aviation market: The progress so far. Journal of Transport Geography, 50, 4–11. https://doi.org/10.1016/j.jtrangeo.2015.05.009spa
dc.relation.referencesO’Connor, K. (2003). Global air travel: toward concentration or dispersal? Journal of Transport Geography, 11(2), 83–92. https://doi.org/10.1016/S0966-6923(03)00002-4spa
dc.relation.referencesO’Kelly, M. (1998). A geographer’s analysis of hub-and-spoke networks. Journal of Transport Geography, 6(3), 171–186. https://doi.org/10.1016/S0966-6923(98)00010-6spa
dc.relation.referencesOliveira, A. V. M., Lohmann, G., & Costa, T. G. (2016). Network concentration and airport congestion in a post de-regulation context: A case study of Brazil 2000–2010. Journal of Transport Geography, 50, 33–44. https://doi.org/10.1016/j.jtrangeo.2015.01.001spa
dc.relation.referencesOum, T.,Yu, C., & Zhang, A. (2001). Global airline alliances: international regulatory issues. Journal of Air Transport Management, 7(1), 57–62. https://doi.org/10.1016/S0969-6997(00)00034-Xspa
dc.relation.referencesOum, T., Zhang, A., & Zhang, Y. (1996). Optimal airport pricing in the hub-and-spoke network. Transportation Research B, 30(1), 11–18. https://doi.org/10.1016/0191-2615(95)00023-2spa
dc.relation.referencesOum, T. (1991). Airline deregulation in Canada. In K. Button (Ed.), Airline deregulation: international experiences (pp. 124– 187). David Fulton Publishers. https://doi.org/10.4324/9781315212036-5spa
dc.relation.referencesPapatheodorou, A., & Arvanitis, P. (2009). Spatial evolution of airport traffic and air transport liberalisation: the case of Greece. Journal of Transport Geography, 17, 402–412. https://doi.org/10.1016/j.jtrangeo.2008.08.004spa
dc.relation.referencesPlummer, M. (2003, March 20–22). JAGS: A program for analysis of Bayesian graphical model using Gibbs sampling. In K. Hornik, F. Leisch, & A. Zeileis (Eds.), Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC 2003). Vienna, Austria.spa
dc.relation.referencesPlummer, M., Best, N., Cowles, K., & Vines, K. (2006). CODA: Convergence Diagnosis and Output Analysis for MCMC. R News, 6(1), 7–11.spa
dc.relation.referencesPetris, G., Petrone, S., & Campagnoli, P. (2009). Dynamic linear models with R. Springer. https://doi.org/10.1007/b135794_2spa
dc.relation.referencesPole, A., West, M., & Harrison, J. (2018). Applied Bayesian forecasting and time series analysis. Chapman and Hall/CRC. https://doi.org/10.1201/9781315274775spa
dc.relation.referencesRen, L., & Glasure, Y. (2009). Applicability of the revised mean absolute percentage errors (mape) approach to some popular normal and non-normal independent time series. International Advances in Economic Research, 15(4), 409. https://doi.org/10.1007/s11294-009-9233-8spa
dc.relation.referencesRolim, P. S. W., Bettini, H. F. A. J., & Oliveira, A.V. M. (2016). Estimating the impact of airport privatization on airline demand: A regression-based event study. Journal of Air Transport Management, 54, 31–41. https://doi.org/10.1016/j.jairtraman.2016.03.019spa
dc.relation.referencesSamagaio, A., & Wolters, M. (2010). Comparative analysis of government forecasts for the Lisbon Airport. Journal of Air Transport Management, 16, 213–217. https://doi.org/10.1016/j.jairtraman.2009.09.002spa
dc.relation.referencesSargan, J., & Bhargava, A. (1983). Testing residuals from least squares regression for being generated by the Gaussian Random Walk. Econometrica, 51, 153–174. https://doi.org/10.2307/1912252spa
dc.relation.referencesShaw, S. L., & Ivy, R. (1994). Airline mergers and their effect on network structure. Journal of Transport Geography, 2(4), 234–246. https://doi.org/10.1016/0966-6923(94)90048-5spa
dc.relation.referencesShaw, S. L. (1993). Hub structures of major U.S. passenger airlines. Journal of Transport Geography, 1(1), 47–58. https://doi.org/10.1016/0966-6923(93)90037-Zspa
dc.relation.referencesStavins, J. (2001). Price determination in the airline market: the effect of market concentration. The Review of Economics and Statistics, 83(1), 200–202. https://doi.org/10.1162/rest.2001.83.1.200spa
dc.relation.referencesSurovitskikh, S., & Lubbe, B. (2015). The Air Liberalisation Index as a tool in measuring the impact of South Africa’s aviation policy in Africa on air passenger traffic flows. Journal of Air Transport Management, 42, 159–166. https://doi.org/10.1016/j.jairtraman.2014.09.010spa
dc.relation.referencesThompson, I. (2002). Air transport liberalization and the development of third level airports in France. Journal of Transport Geography, 10(4), 273–285. https://doi.org/10.1016/S0966-6923(02)00043-1spa
dc.relation.referencesTsui, W. H. K., Ozer Balli, H., Gilbey, A., & Gow, H. (2014). Forecasting of Hong Kong airport’s passenger throughput. Tourism Management, 42, 62–76. https://doi.org/10.1016/j.tourman.2013.10.008spa
dc.relation.referencesValencia, M., & Correa, J. (2013). Un Modelo Dinámico Bayesiano para el Pronóstico de Energía Diaria. Revista Ingeniería Industrial, 12(2), 7–17.spa
dc.relation.referencesVowles, T. (2006). Airfare pricing determinants in hub-to-hub markets. Journal of Transport Geography, 14(1), 15–22. https://doi.org/10.1016/j.jtrangeo.2004.10.004spa
dc.relation.referencesVowles, T. (2000). The geographic effects of US airline alliances. Journal of Transport Geography, 8(4), 277–285. https://doi.org/10.1016/S0966-6923(00)00012-0spa
dc.relation.referencesWei, W. (2006). Time series analysis univariate and multivariate methods. Pearson Addison Wesleyspa
dc.relation.referencesWest, M., & Harrison, J. (2006). Bayesian forecasting and dynamic models. Springer Science & Business Media.spa
dc.relation.referencesXiao, Y., Liu, J. J., Hu, Y., Wang, Y., Lai, K. K., & Wang, S. (2014). A neuro-fuzzy combination model based on singular spectrum analysis for air transport demand forecasting. Journal of Air Transport Management, 39, 1–11. https://doi.org/10.1016/j.jairtraman.2014.03.004spa
dc.relation.referencesYoussef, W., & Hansen, M. (1994). Consequences of strategic alliances between international airlines: the case of Swissair and SAS. Transportation Research A, 28(5), 415–431. https://doi.org/10.1016/0965-8564(94)90024-8spa
dc.rightsAtribución-NoComercial-SinDerivadas 2.5 Colombia*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/co/*
dc.subject.keywordair traffic forecastspa
dc.subject.keywordliberalizationspa
dc.subject.keywordDynamic Linear Modelsspa
dc.subject.keywordairportspa
dc.subject.keywordair transportspa
dc.subject.keywordColombiaspa
dc.titleAir traffic forecast in post-liberalization context: a dynamic linear models approachspa
dc.type.categoryGeneración de Nuevo Conocimiento: Artículos publicados en revistas especializadas - Electrónicosspa

Archivos

Bloque original

Mostrando 1 - 1 de 1
Cargando...
Miniatura
Nombre:
Articulo Aviation 2020 - 3.pdf
Tamaño:
467.36 KB
Formato:
Adobe Portable Document Format
Descripción:

Bloque de licencias

Mostrando 1 - 1 de 1
Thumbnail USTA
Nombre:
license.txt
Tamaño:
807 B
Formato:
Item-specific license agreed upon to submission
Descripción: