Evaluación de modelos de series de tiempo para pronosticar la demanda de transporte aéreo a corto y mediano plazo en Colombia

ilustraciones, gráficas, tablas

Autores:: Amezquita Bravo, Cristian Camilo

Tipo de recurso:

Fecha de publicación:: 2021

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

id	UNACIONAL2_7689e731c03cadf90d270c14d8c7b36e
oai_identifier_str	oai:repositorio.unal.edu.co:unal/81124
network_acronym_str	UNACIONAL2
network_name_str	Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv	Evaluación de modelos de series de tiempo para pronosticar la demanda de transporte aéreo a corto y mediano plazo en Colombia
dc.title.translated.eng.fl_str_mv	Evaluation of time series models to forecast air transportation demand in the short and medium term in Colombia
title	Evaluación de modelos de series de tiempo para pronosticar la demanda de transporte aéreo a corto y mediano plazo en Colombia
spellingShingle	Evaluación de modelos de series de tiempo para pronosticar la demanda de transporte aéreo a corto y mediano plazo en Colombia 330 - Economía Time-series analysis Aeronautics, commercial Forecasting techniques Análisis de series de tiempo Aviación comercial Técnicas de predicción Pronóstico de demanda Transporte aéreo Series de tiempo ARIMA SARIMA ARIMAX Forecasting demand Air transport Air passengers demand ARIMA SARIMA ARIMAX Time series
title_short	Evaluación de modelos de series de tiempo para pronosticar la demanda de transporte aéreo a corto y mediano plazo en Colombia
title_full	Evaluación de modelos de series de tiempo para pronosticar la demanda de transporte aéreo a corto y mediano plazo en Colombia
title_fullStr	Evaluación de modelos de series de tiempo para pronosticar la demanda de transporte aéreo a corto y mediano plazo en Colombia
title_full_unstemmed	Evaluación de modelos de series de tiempo para pronosticar la demanda de transporte aéreo a corto y mediano plazo en Colombia
title_sort	Evaluación de modelos de series de tiempo para pronosticar la demanda de transporte aéreo a corto y mediano plazo en Colombia
dc.creator.fl_str_mv	Amezquita Bravo, Cristian Camilo
dc.contributor.advisor.spa.fl_str_mv	Moreno Rivas, Álvaro Martin
dc.contributor.author.spa.fl_str_mv	Amezquita Bravo, Cristian Camilo
dc.subject.ddc.spa.fl_str_mv	330 - Economía
topic	330 - Economía Time-series analysis Aeronautics, commercial Forecasting techniques Análisis de series de tiempo Aviación comercial Técnicas de predicción Pronóstico de demanda Transporte aéreo Series de tiempo ARIMA SARIMA ARIMAX Forecasting demand Air transport Air passengers demand ARIMA SARIMA ARIMAX Time series
dc.subject.lemb.eng.fl_str_mv	Time-series analysis Aeronautics, commercial Forecasting techniques
dc.subject.lemb.spa.fl_str_mv	Análisis de series de tiempo Aviación comercial Técnicas de predicción
dc.subject.proposal.spa.fl_str_mv	Pronóstico de demanda Transporte aéreo Series de tiempo ARIMA SARIMA ARIMAX
dc.subject.proposal.eng.fl_str_mv	Forecasting demand Air transport Air passengers demand ARIMA SARIMA ARIMAX Time series
description	ilustraciones, gráficas, tablas
publishDate	2021
dc.date.issued.none.fl_str_mv	2021
dc.date.accessioned.none.fl_str_mv	2022-03-03T16:58:34Z
dc.date.available.none.fl_str_mv	2022-03-03T16:58:34Z
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/81124
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/81124 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.references.spa.fl_str_mv	Aeronáutica Civil, (2014a). Guía para la elaboración de Planes Maestros Aeroportuarios – PMA, Circular Reglamentaria N° 053, https://www.aerocivil.gov.co/normatividad/CIRCULARES%20AGA/CI%20053%20-%20V2.pdf Aeronáutica Civil, (2014b). Actualización del plan maestro del Aeropuerto Internacional ElDorado reporte final, Tylin international, https://www.aerocivil.gov.co/aeropuertos/_layouts/15/WopiFrame,aspx?sourcedoc=/aeropuertos/Consecionados/El%20Dorado%20-%20Bogot%C3%A1,pdf&action=default Aeronáutica Civil, (2019). Aeropuertos – Planes Maestro, https://www.aerocivil.gov.co/aeropuertos/planes%20maestros/forms/allitems.aspx Alhassan, R., Abdulaal, R., & Alsulami, H. (2017). A Forecasting Model for Satisfying the Demand of International Flight Passengers Having Domestic Flight Connection. Journal of King Abdulaziz University Engineering Sciences, 28(1), 49–63. https://doi.org/10.4197/eng.28-1.4 Andreoni, A., & Postorino, M. N. (2006). a Multivariate Arima Model To Forecast. Artis, M. J., Clavel, J. G., Hoffmann, M., & Nachane, D. M. (2007). Harmonic Regression Models: A Comparative Review with Applications. SSRN Electronic Journal, 333. https://doi.org/10.2139/ssrn.1017519 Bloomfield, P. (2000). Fourier Analysis of Time Series: An Introduction. In Journal of the American Statistical Association (Vol. 95, Issue 452, p. 1373). https://doi.org/10.2307/2669794 Chatfield, C. (2000). Time-Series Forecasting. In Urologiia (Moscow, Russia : 1999) (Issue 1). Chapman & Hall/CRC. http://www.ncbi.nlm.nih.gov/pubmed/16856455 Chen, C. F., Chang, Y. H., & Chang, Y. W. (2009). Seasonal ARIMA forecasting of inbound air travel arrivals to Taiwan. Transportmetrica, 5(2), 125–140. https://doi.org/10.1080/18128600802591210 Chow, G. C., & Lin, A. (1971). Best Linear Unbiased Interpolation, Distribution, and Extrapolation of Time Series by Related Series. The Review of Economics and Statistics, 53(4), 372. https://doi.org/10.2307/1928739 Coshall, J. (2006). Time series analyses of UK outbound travel by air. Journal of Travel Research, 44(3), 335–347. https://doi.org/10.1177/0047287505279003 Cowpertwait, P., & Metcalfe, A. (2009). Introductory Time Series with R. Springer. http://www.springer.com/la/book/9780387886978%0Ahttp://www.springer.com/la/book/9780387886978?wt_mc=ThirdParty.SpringerLink.3.EPR653.About_eBook Dantas, T. M., Cyrino Oliveira, F. L., & Varela Repolho, H. M. (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.2016.12.006 Denton, F. T. (1971). Adjustment of monthly or quarterly series to annual totals: An approach based on quadratic minimization. Journal of the American Statistical Association, 66(333), 99–102. https://doi.org/10.1080/01621459.1971.10482227 Díaz Olariaga, O., Girón Amaya, E., & Mora-Camino, F. (2017, October). Pronóstico de la demanda de pasajeros en aeropuertos privatizados. In VI Congreso Internacional de la Red Iberoamericana de Investigación en Transporte Aéreo (pp. 10-12). https://www.researchgate.net/publication/320434160_PRONOSTICO_DE_LA_DEMANDA_DE_PASAJEROS_EN_AEROPUERTOS_PRIVATIZADOS Díaz Olariaga, Ó. (2016). Análisis del desarrollo reciente del transporte aéreo en Colombia. Revista Transporte y Territorio, 0(14), 122–143. https://doi.org/10.34096/rtt.i14.2432 du Preez, J., & Witt, S. F. (2003). Univariate versus multivariate time series forecasting: An application to international tourism demand. International Journal of Forecasting, 19(3), 435–451. https://doi.org/10.1016/S0169-2070(02)00057-2 Emiray, E.; Rodríguez, G. (2003). Evaluating time series models in short and long-term forecasting of Canadian air passenger data. No. 0306E, January 2003. Franses, P. H. (1990). TESTING FOR SEASONAL UNIT ROOTS IN MONTHLY DATA. Erasmus University Rotterdam. Goh, C., & Law, R. (2002). Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention. Tourism Management, 23(5), 499–510. https://doi.org/10.1016/S0261-5177(02)00009-2 Granger, C. W. J., & Newbold, P. (1974). Spurious Regressions in Econometrics. Journal of Econometrics, 2, 111–120. https://doi.org/10.1002/9780470996249.ch27 Greene, W. H. (1999). Análisis Econométrico (3rd ed.). Prentice Hall. Gujarati, D., & Porter, D. (2010). Econometría (Quinta edi). McGraw Hill. Hamilton, J. (1994). Time Series Analysis. Princeton University Press. Harvey, A. C. (1993). Time Series Models. In Angewandte Chemie International Edition (2nd Editio, Vol. 6, Issue 11). MIT Press. Hylleberg, S., Engle, R. F., Granger, C. W. J., & Yoo, B. S. (1990). Seasonal integration and cointegration. Journal of Econometrics, 44(1–2), 215–238. https://doi.org/10.1016/0304-4076(90)90080-D IATA. (2007). Aviation Economic Benefits. IATA Economics Briefing, N° 8. IATA. (2020). Air connectivity: Measuring the connections that drive economic growth. 57(2), Contents2–Contents2. https://doi.org/10.3143/geriatrics.57.contents2 Kim, J. H., & Moosa, I. (2001). Seasonal behaviour of monthly international tourist flows: Specification and implications for forecasting models. Tourism Economics, 7(4), 381–396. https://doi.org/10.5367/000000001101297937 Lim, C., & Mcaleer, M. (2010). A seasonal analysis of Asian tourist arrivals to Australia. Applied Economics, 32(4), 499–509. https://doi.org/10.1080/000368400322660 Lim, C., & McAleer, M. (2001). Forecasting tourist arrivals. Annals of Tourism Research, 28(4), 965–977. https://doi.org/10.1016/S0160-7383(01)00006-8 Lin, C. J., Chen, H. F., & Lee, T. S. (2011). Forecasting Tourism Demand Using Time Series, Artificial Neural Networks and Multivariate Adaptive Regression Splines:Evidence from Taiwan. International Journal of Business Administration, 2(2). https://doi.org/10.5430/ijba.v2n2p14 Lutkepohl, H., & Kratzig, M. (2013). Applied Time Series Econometrics Time. In Journal of Chemical Information and Modeling (Vol. 53, Issue 9). Cambridge University Press. Marazzo, M., Scherre, R., & Fernandes, E. (2010). Air transport demand and economic growth in Brazil: A time series analysis. Transportation Research Part E: Logistics and Transportation Review, 46(2), 261–269. https://doi.org/10.1016/j.tre.2009.08.008 Martínez-Ortíz, A., & García-Romero, H. (2016). Competitividad en el transporte aéreo en Colombia. 218. https://www.repository.fedesarrollo.org.co/bitstream/handle/11445/3280/Repor_Junio_2016_Martinez_y_Garcia.pdf?sequence=2&isAllowed=y&fbclid=IwAR2zdrF15WDXB7QHagHNepZLhhwPxF5nAE-06gXbchtCeFt6oq1I8ChgYGo%0Ahttp://www.repository.fedesarrollo.org.co/handle/11 Mogollón, J. D. (2020). Pronóstico de la demanda del transporte aéreo en aeropuerto distribuidor. Aplicación al caso de Aeropuerto Internacional el Dorado. 15, 1–137. Nieto, M. R., & Carmona-Benítez, R. B. (2018). ARIMA + GARCH + Bootstrap forecasting method applied to the airline industry. Journal of Air Transport Management, 71(June), 1–8. https://doi.org/10.1016/j.jairtraman.2018.05.007 Oh, C. O., & Morzuch, B. J. (2005). Evaluating time-series models to forecast the demand for tourism in Singapore: Comparing within-sample and postsample results. Journal of Travel Research, 43(4), 404–413. https://doi.org/10.1177/0047287505274653 Olivera, M., Cabrera, P., Bermúdez, W., & Hernandez, A. (2011). El impacto del transporte aéreo en la economía colombiana y las políticas públicas. In Cuaderno de fedesarrollo N.34. www.atac.aero/imagenesindex/cuaderno_fedesarrollo_34.pdf Ramos, I., & Cardenas, L. (2016). Prognosis de tráfico en el Aeropuerto de Bogotá-El Dorado y análisis de la relación demanda-capacidad. Sharif Azadeh, S., Labib, R., & Savard, G. (2013). Railway demand forecasting in revenue management using neural networks. International Journal of Revenue Management, 7(1), 18–36. https://doi.org/10.1504/IJRM.2013.053358 Straszheim, M. (1978). Airline demand functions in the North Atlantic and their pricing implications. Journal of Transport Economics and Policy, 12(2), 179–195. The R Development Core Team. (2008). R: A Language and Environment for Statistical Computing: Vol. Version 2. https://www.r-project.org/ Tsui, W. H. K., Ozer Balli, H., Gilbey, A., & Gow, H. (2014). Forecasting of Hong Kong airport’s passenger throughput. Tourism Management, 42(2014), 62–76. https://doi.org/10.1016/j.tourman.2013.10.008 United States Bureau Census. (2021). X-13ARIMA-SEATS Reference Manual Accessible HTML Output Version. Wei, W. (2006). Time Series Analysis: Univariate and Multivariate Methods. In Technometrics (Vol. 33, Issue 1). Pearson. https://doi.org/10.1080/00401706.1991.10484777 Williams, B. M. (2001). Multivariate Vehicular Traffic Flow Prediction: Evaluation of ARIMAX Modeling. Transportation Research Record, 01, 194–200. Yule, G. U. (1926). Why do we Sometimes get Nonsense-Correlations between Time-Series?: A Study in Sampling and the Nature of Time-Series. Journal of the Royal Statistical Society, 89(1), 1–63.
dc.rights.coar.fl_str_mv	http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv	Atribución-NoComercial-SinDerivadas 4.0 Internacional
dc.rights.uri.spa.fl_str_mv	http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.accessrights.spa.fl_str_mv	info:eu-repo/semantics/openAccess
rights_invalid_str_mv	Atribución-NoComercial-SinDerivadas 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv	openAccess
dc.format.extent.spa.fl_str_mv	x, 46 páginas
dc.format.mimetype.spa.fl_str_mv	application/pdf
dc.coverage.country.spa.fl_str_mv	Colombia
dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Bogotá - Ciencias Económicas - Maestría en Ciencias Económicas
dc.publisher.department.spa.fl_str_mv	Escuela de Economía
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ciencias Económicas
dc.publisher.place.spa.fl_str_mv	Bogotá, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Bogotá
institution	Universidad Nacional de Colombia
bitstream.url.fl_str_mv	https://repositorio.unal.edu.co/bitstream/unal/81124/3/1018467917.2021.pdf https://repositorio.unal.edu.co/bitstream/unal/81124/4/license.txt https://repositorio.unal.edu.co/bitstream/unal/81124/5/1018467917.2021.pdf.jpg
bitstream.checksum.fl_str_mv	97a1d3ff7678d42fdf7b80b3d181ed51 8153f7789df02f0a4c9e079953658ab2 db943816ca04a69934eb2c2327a8ca99
bitstream.checksumAlgorithm.fl_str_mv	MD5 MD5 MD5
repository.name.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
repository.mail.fl_str_mv	repositorio_nal@unal.edu.co
_version_	1814089827266592768
spelling	Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Moreno Rivas, Álvaro Martin75e8c605f438ea79d3e97f0d61ed2d5fAmezquita Bravo, Cristian Camilo38d502586f0357ae4c3076c51706333f2022-03-03T16:58:34Z2022-03-03T16:58:34Z2021https://repositorio.unal.edu.co/handle/unal/81124Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, gráficas, tablasEl presente trabajo suministra una evaluación de la capacidad predictiva de diferentes modelos de serie de tiempo en datos mensuales del transporte aéreo de pasajeros de tráfico nacional, internacional y total entre 1994 y el 2019. Los modelos estimados son: modelo de regresión armónica, modelo de suavizado exponencial de Holt-Winters, modelo autorregresivo integrado de media móvil (ARIMA), ARIMA estacional (SARIMA) y ARIMA con variable exógena (ARIMAX). Los resultados muestran que los modelos SARIMA y SARIMAX proveen los mejores resultados en cuanto a bondad de ajuste y precisión con pronósticos en términos de MAPE y RMSE por debajo del umbral del 3% de la realización puntual media. El modelo multivariado SARIMAX supera los resultados de pronóstico de los modelos univariantes. El PIB logra potenciar los resultados del modelo y se confirma la relación positiva que posee con el sector aéreo. Se evaluaron otras variables como los precios del petróleo y choques exógenos locales e internacionales pero su efecto resultó ser no significativo. El modelo de regresión armónica solo puede predecir con alta precisión los pasajeros de tráfico internacional mientras que el modelo de Holt Winters logra obtener previsiones altamente precisas para la serie de tráfico internacional y total. (Texto tomado de la fuente).This thesis provides an evaluation of the predictive capacity of different time series models in monthly data of the air transport of passengers of national, international, and total traffic between 1994 and 2019. The estimated models are harmonic regression model, Holt-Winters exponential smoothing model, integrated moving average autoregressive model (ARIMA), seasonal ARIMA (SARIMA) and ARIMA with exogenous variable (ARIMAX). The results show that the SARIMA and SARIMAX models provide the best results in terms of goodness of fit and precision with forecasts in terms of MAPE and RMSE below the threshold of 3% of the average punctual realization. The SARIMAX multivariate model exceeds the forecast results of the univariate models. The GDP manages to enhance the results of the model and the positive relationship it has with the airline sector is confirmed. Other variables such as oil prices and local and international exogenous shocks were evaluated, but their effect was not significant. The harmonic regression model can only predict international traffic passengers with high precision while the Holt Winters model manages to obtain highly accurate forecasts for the international and total traffic series.MaestríaMagíster en Ciencias Económicasx, 46 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias Económicas - Maestría en Ciencias EconómicasEscuela de EconomíaFacultad de Ciencias EconómicasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá330 - EconomíaTime-series analysisAeronautics, commercialForecasting techniquesAnálisis de series de tiempoAviación comercialTécnicas de predicciónPronóstico de demandaTransporte aéreoSeries de tiempoARIMASARIMAARIMAXForecasting demandAir transportAir passengers demandARIMASARIMAARIMAXTime seriesEvaluación de modelos de series de tiempo para pronosticar la demanda de transporte aéreo a corto y mediano plazo en ColombiaEvaluation of time series models to forecast air transportation demand in the short and medium term in ColombiaTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMColombiaAeronáutica Civil, (2014a). Guía para la elaboración de Planes Maestros Aeroportuarios – PMA, Circular Reglamentaria N° 053, https://www.aerocivil.gov.co/normatividad/CIRCULARES%20AGA/CI%20053%20-%20V2.pdfAeronáutica Civil, (2014b). Actualización del plan maestro del Aeropuerto Internacional ElDorado reporte final, Tylin international, https://www.aerocivil.gov.co/aeropuertos/_layouts/15/WopiFrame,aspx?sourcedoc=/aeropuertos/Consecionados/El%20Dorado%20-%20Bogot%C3%A1,pdf&action=defaultAeronáutica Civil, (2019). Aeropuertos – Planes Maestro, https://www.aerocivil.gov.co/aeropuertos/planes%20maestros/forms/allitems.aspxAlhassan, R., Abdulaal, R., & Alsulami, H. (2017). A Forecasting Model for Satisfying the Demand of International Flight Passengers Having Domestic Flight Connection. Journal of King Abdulaziz University Engineering Sciences, 28(1), 49–63. https://doi.org/10.4197/eng.28-1.4Andreoni, A., & Postorino, M. N. (2006). a Multivariate Arima Model To Forecast.Artis, M. J., Clavel, J. G., Hoffmann, M., & Nachane, D. M. (2007). Harmonic Regression Models: A Comparative Review with Applications. SSRN Electronic Journal, 333. https://doi.org/10.2139/ssrn.1017519Bloomfield, P. (2000). Fourier Analysis of Time Series: An Introduction. In Journal of the American Statistical Association (Vol. 95, Issue 452, p. 1373). https://doi.org/10.2307/2669794Chatfield, C. (2000). Time-Series Forecasting. In Urologiia (Moscow, Russia : 1999) (Issue 1). Chapman & Hall/CRC. http://www.ncbi.nlm.nih.gov/pubmed/16856455Chen, C. F., Chang, Y. H., & Chang, Y. W. (2009). Seasonal ARIMA forecasting of inbound air travel arrivals to Taiwan. Transportmetrica, 5(2), 125–140. https://doi.org/10.1080/18128600802591210Chow, G. C., & Lin, A. (1971). Best Linear Unbiased Interpolation, Distribution, and Extrapolation of Time Series by Related Series. The Review of Economics and Statistics, 53(4), 372. https://doi.org/10.2307/1928739Coshall, J. (2006). Time series analyses of UK outbound travel by air. Journal of Travel Research, 44(3), 335–347. https://doi.org/10.1177/0047287505279003Cowpertwait, P., & Metcalfe, A. (2009). Introductory Time Series with R. Springer. http://www.springer.com/la/book/9780387886978%0Ahttp://www.springer.com/la/book/9780387886978?wt_mc=ThirdParty.SpringerLink.3.EPR653.About_eBookDantas, T. M., Cyrino Oliveira, F. L., & Varela Repolho, H. M. (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.2016.12.006Denton, F. T. (1971). Adjustment of monthly or quarterly series to annual totals: An approach based on quadratic minimization. Journal of the American Statistical Association, 66(333), 99–102. https://doi.org/10.1080/01621459.1971.10482227Díaz Olariaga, O., Girón Amaya, E., & Mora-Camino, F. (2017, October). Pronóstico de la demanda de pasajeros en aeropuertos privatizados. In VI Congreso Internacional de la Red Iberoamericana de Investigación en Transporte Aéreo (pp. 10-12). https://www.researchgate.net/publication/320434160_PRONOSTICO_DE_LA_DEMANDA_DE_PASAJEROS_EN_AEROPUERTOS_PRIVATIZADOSDíaz Olariaga, Ó. (2016). Análisis del desarrollo reciente del transporte aéreo en Colombia. Revista Transporte y Territorio, 0(14), 122–143. https://doi.org/10.34096/rtt.i14.2432du Preez, J., & Witt, S. F. (2003). Univariate versus multivariate time series forecasting: An application to international tourism demand. International Journal of Forecasting, 19(3), 435–451. https://doi.org/10.1016/S0169-2070(02)00057-2Emiray, E.; Rodríguez, G. (2003). Evaluating time series models in short and long-term forecasting of Canadian air passenger data. No. 0306E, January 2003.Franses, P. H. (1990). TESTING FOR SEASONAL UNIT ROOTS IN MONTHLY DATA. Erasmus University Rotterdam.Goh, C., & Law, R. (2002). Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention. Tourism Management, 23(5), 499–510. https://doi.org/10.1016/S0261-5177(02)00009-2Granger, C. W. J., & Newbold, P. (1974). Spurious Regressions in Econometrics. Journal of Econometrics, 2, 111–120. https://doi.org/10.1002/9780470996249.ch27Greene, W. H. (1999). Análisis Econométrico (3rd ed.). Prentice Hall.Gujarati, D., & Porter, D. (2010). Econometría (Quinta edi). McGraw Hill.Hamilton, J. (1994). Time Series Analysis. Princeton University Press.Harvey, A. C. (1993). Time Series Models. In Angewandte Chemie International Edition (2nd Editio, Vol. 6, Issue 11). MIT Press.Hylleberg, S., Engle, R. F., Granger, C. W. J., & Yoo, B. S. (1990). Seasonal integration and cointegration. Journal of Econometrics, 44(1–2), 215–238. https://doi.org/10.1016/0304-4076(90)90080-DIATA. (2007). Aviation Economic Benefits. IATA Economics Briefing, N° 8.IATA. (2020). Air connectivity: Measuring the connections that drive economic growth. 57(2), Contents2–Contents2. https://doi.org/10.3143/geriatrics.57.contents2Kim, J. H., & Moosa, I. (2001). Seasonal behaviour of monthly international tourist flows: Specification and implications for forecasting models. Tourism Economics, 7(4), 381–396. https://doi.org/10.5367/000000001101297937Lim, C., & Mcaleer, M. (2010). A seasonal analysis of Asian tourist arrivals to Australia. Applied Economics, 32(4), 499–509. https://doi.org/10.1080/000368400322660Lim, C., & McAleer, M. (2001). Forecasting tourist arrivals. Annals of Tourism Research, 28(4), 965–977. https://doi.org/10.1016/S0160-7383(01)00006-8Lin, C. J., Chen, H. F., & Lee, T. S. (2011). Forecasting Tourism Demand Using Time Series, Artificial Neural Networks and Multivariate Adaptive Regression Splines:Evidence from Taiwan. International Journal of Business Administration, 2(2). https://doi.org/10.5430/ijba.v2n2p14Lutkepohl, H., & Kratzig, M. (2013). Applied Time Series Econometrics Time. In Journal of Chemical Information and Modeling (Vol. 53, Issue 9). Cambridge University Press.Marazzo, M., Scherre, R., & Fernandes, E. (2010). Air transport demand and economic growth in Brazil: A time series analysis. Transportation Research Part E: Logistics and Transportation Review, 46(2), 261–269. https://doi.org/10.1016/j.tre.2009.08.008Martínez-Ortíz, A., & García-Romero, H. (2016). Competitividad en el transporte aéreo en Colombia. 218. https://www.repository.fedesarrollo.org.co/bitstream/handle/11445/3280/Repor_Junio_2016_Martinez_y_Garcia.pdf?sequence=2&isAllowed=y&fbclid=IwAR2zdrF15WDXB7QHagHNepZLhhwPxF5nAE-06gXbchtCeFt6oq1I8ChgYGo%0Ahttp://www.repository.fedesarrollo.org.co/handle/11Mogollón, J. D. (2020). Pronóstico de la demanda del transporte aéreo en aeropuerto distribuidor. Aplicación al caso de Aeropuerto Internacional el Dorado. 15, 1–137.Nieto, M. R., & Carmona-Benítez, R. B. (2018). ARIMA + GARCH + Bootstrap forecasting method applied to the airline industry. Journal of Air Transport Management, 71(June), 1–8. https://doi.org/10.1016/j.jairtraman.2018.05.007Oh, C. O., & Morzuch, B. J. (2005). Evaluating time-series models to forecast the demand for tourism in Singapore: Comparing within-sample and postsample results. Journal of Travel Research, 43(4), 404–413. https://doi.org/10.1177/0047287505274653Olivera, M., Cabrera, P., Bermúdez, W., & Hernandez, A. (2011). El impacto del transporte aéreo en la economía colombiana y las políticas públicas. In Cuaderno de fedesarrollo N.34. www.atac.aero/imagenesindex/cuaderno_fedesarrollo_34.pdfRamos, I., & Cardenas, L. (2016). Prognosis de tráfico en el Aeropuerto de Bogotá-El Dorado y análisis de la relación demanda-capacidad.Sharif Azadeh, S., Labib, R., & Savard, G. (2013). Railway demand forecasting in revenue management using neural networks. International Journal of Revenue Management, 7(1), 18–36. https://doi.org/10.1504/IJRM.2013.053358Straszheim, M. (1978). Airline demand functions in the North Atlantic and their pricing implications. Journal of Transport Economics and Policy, 12(2), 179–195.The R Development Core Team. (2008). R: A Language and Environment for Statistical Computing: Vol. Version 2. https://www.r-project.org/Tsui, W. H. K., Ozer Balli, H., Gilbey, A., & Gow, H. (2014). Forecasting of Hong Kong airport’s passenger throughput. Tourism Management, 42(2014), 62–76. https://doi.org/10.1016/j.tourman.2013.10.008United States Bureau Census. (2021). X-13ARIMA-SEATS Reference Manual Accessible HTML Output Version.Wei, W. (2006). Time Series Analysis: Univariate and Multivariate Methods. In Technometrics (Vol. 33, Issue 1). Pearson. https://doi.org/10.1080/00401706.1991.10484777Williams, B. M. (2001). Multivariate Vehicular Traffic Flow Prediction: Evaluation of ARIMAX Modeling. Transportation Research Record, 01, 194–200.Yule, G. U. (1926). Why do we Sometimes get Nonsense-Correlations between Time-Series?: A Study in Sampling and the Nature of Time-Series. Journal of the Royal Statistical Society, 89(1), 1–63.Público generalORIGINAL1018467917.2021.pdf1018467917.2021.pdfTesis de Maestría en Ciencias Económicasapplication/pdf1391521https://repositorio.unal.edu.co/bitstream/unal/81124/3/1018467917.2021.pdf97a1d3ff7678d42fdf7b80b3d181ed51MD53LICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/81124/4/license.txt8153f7789df02f0a4c9e079953658ab2MD54THUMBNAIL1018467917.2021.pdf.jpg1018467917.2021.pdf.jpgGenerated Thumbnailimage/jpeg5134https://repositorio.unal.edu.co/bitstream/unal/81124/5/1018467917.2021.pdf.jpgdb943816ca04a69934eb2c2327a8ca99MD55unal/81124oai:repositorio.unal.edu.co:unal/811242023-08-02 23:03:47.241Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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

Evaluación de modelos de series de tiempo para pronosticar la demanda de transporte aéreo a corto y mediano plazo en Colombia

Publicaciones similares