Aplicación de modelos arima para la proyección del recaudo del impuesto de industria, comercio, avisos y tableros ICA

Con el fin de pronosticar el comportamiento del recaudo del impuesto de industria, comercio, avisos y tableros (ICA), para el año 2016, se plantea la metodología de modelos ARIMA, por su capacidad de predicción a corto plazo. Se definen cuatro fases: La primera es la identificación, describiendo el...

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Autores:
Rosas Gualdron, Adriana Marcela
Tipo de recurso:
http://purl.org/coar/version/c_b1a7d7d4d402bcce
Fecha de publicación:
2016
Institución:
Universidad Industrial de Santander
Repositorio:
Repositorio UIS
Idioma:
spa
OAI Identifier:
oai:noesis.uis.edu.co:20.500.14071/35510
Acceso en línea:
https://noesis.uis.edu.co/handle/20.500.14071/35510
https://noesis.uis.edu.co
Palabra clave:
Recaudo
Impuesto
Estacionariedad
Estacionalidad
Modelo Arima (Modelo Autoregresivo Integrado De Media Móvil)
Proyección.
In order to predict the behavior of tax collection on industry
commerce
boards and advertising (ICA) for the year 2016
the methodology of ARIMA models arises by their ability to predict short term. In order to achieve the goal there are four different phases: The first is identifying
describing the behavior of the historical collection and assess the possibility of converting the stationary series both seasonal and regular part. The second phase consists of the estimation model
taking into account the criteria AIC
small errors and randomness of residuals. The third is validation: at this stage the 2015 collection whose figures already taken have been caused and as the screening of this year in contrast to ensure the quality of the model. The last phase is the prediction
in which case the collection is expected by 2016
taking a confidence interval of 80%. It should be noted that collection is made up the value to pay more interest on arrears and is expressed in thousands of pesos. The frequency of the time series analyzed bimonthly seasonal variation is a very important characteristic to be accounted for in a time series forecasting model. Traditionally
the role of autocorrelation function is used to identify if the process is stationary and then the partial autocorrelation function is used to detect the model. Once
guaranteed the stationarity of the process provides that the historical model behavior of the series is set in an . To validate if the selected model is appropriate
Box Ljung test
Shapiro Wilk test and integrated periodogram are used. Concluding that the residuals are not correlated
normally distributed and randomly.
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License
Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
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network_acronym_str UISANTADR2
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repository_id_str
dc.title.none.fl_str_mv Aplicación de modelos arima para la proyección del recaudo del impuesto de industria, comercio, avisos y tableros ICA
dc.title.english.none.fl_str_mv Collection, Tax, Stationarity, Seasonality, Arima (Autoregressive Integrated Moving Average Model), Projection.
title Aplicación de modelos arima para la proyección del recaudo del impuesto de industria, comercio, avisos y tableros ICA
spellingShingle Aplicación de modelos arima para la proyección del recaudo del impuesto de industria, comercio, avisos y tableros ICA
Recaudo
Impuesto
Estacionariedad
Estacionalidad
Modelo Arima (Modelo Autoregresivo Integrado De Media Móvil)
Proyección.
In order to predict the behavior of tax collection on industry
commerce
boards and advertising (ICA) for the year 2016
the methodology of ARIMA models arises by their ability to predict short term. In order to achieve the goal there are four different phases: The first is identifying
describing the behavior of the historical collection and assess the possibility of converting the stationary series both seasonal and regular part. The second phase consists of the estimation model
taking into account the criteria AIC
small errors and randomness of residuals. The third is validation: at this stage the 2015 collection whose figures already taken have been caused and as the screening of this year in contrast to ensure the quality of the model. The last phase is the prediction
in which case the collection is expected by 2016
taking a confidence interval of 80%. It should be noted that collection is made up the value to pay more interest on arrears and is expressed in thousands of pesos. The frequency of the time series analyzed bimonthly seasonal variation is a very important characteristic to be accounted for in a time series forecasting model. Traditionally
the role of autocorrelation function is used to identify if the process is stationary and then the partial autocorrelation function is used to detect the model. Once
guaranteed the stationarity of the process provides that the historical model behavior of the series is set in an . To validate if the selected model is appropriate
Box Ljung test
Shapiro Wilk test and integrated periodogram are used. Concluding that the residuals are not correlated
normally distributed and randomly.
title_short Aplicación de modelos arima para la proyección del recaudo del impuesto de industria, comercio, avisos y tableros ICA
title_full Aplicación de modelos arima para la proyección del recaudo del impuesto de industria, comercio, avisos y tableros ICA
title_fullStr Aplicación de modelos arima para la proyección del recaudo del impuesto de industria, comercio, avisos y tableros ICA
title_full_unstemmed Aplicación de modelos arima para la proyección del recaudo del impuesto de industria, comercio, avisos y tableros ICA
title_sort Aplicación de modelos arima para la proyección del recaudo del impuesto de industria, comercio, avisos y tableros ICA
dc.creator.fl_str_mv Rosas Gualdron, Adriana Marcela
dc.contributor.advisor.none.fl_str_mv Orlandoni Merli, Giampaolo
dc.contributor.author.none.fl_str_mv Rosas Gualdron, Adriana Marcela
dc.subject.none.fl_str_mv Recaudo
Impuesto
Estacionariedad
Estacionalidad
Modelo Arima (Modelo Autoregresivo Integrado De Media Móvil)
Proyección.
topic Recaudo
Impuesto
Estacionariedad
Estacionalidad
Modelo Arima (Modelo Autoregresivo Integrado De Media Móvil)
Proyección.
In order to predict the behavior of tax collection on industry
commerce
boards and advertising (ICA) for the year 2016
the methodology of ARIMA models arises by their ability to predict short term. In order to achieve the goal there are four different phases: The first is identifying
describing the behavior of the historical collection and assess the possibility of converting the stationary series both seasonal and regular part. The second phase consists of the estimation model
taking into account the criteria AIC
small errors and randomness of residuals. The third is validation: at this stage the 2015 collection whose figures already taken have been caused and as the screening of this year in contrast to ensure the quality of the model. The last phase is the prediction
in which case the collection is expected by 2016
taking a confidence interval of 80%. It should be noted that collection is made up the value to pay more interest on arrears and is expressed in thousands of pesos. The frequency of the time series analyzed bimonthly seasonal variation is a very important characteristic to be accounted for in a time series forecasting model. Traditionally
the role of autocorrelation function is used to identify if the process is stationary and then the partial autocorrelation function is used to detect the model. Once
guaranteed the stationarity of the process provides that the historical model behavior of the series is set in an . To validate if the selected model is appropriate
Box Ljung test
Shapiro Wilk test and integrated periodogram are used. Concluding that the residuals are not correlated
normally distributed and randomly.
dc.subject.keyword.none.fl_str_mv In order to predict the behavior of tax collection on industry
commerce
boards and advertising (ICA) for the year 2016
the methodology of ARIMA models arises by their ability to predict short term. In order to achieve the goal there are four different phases: The first is identifying
describing the behavior of the historical collection and assess the possibility of converting the stationary series both seasonal and regular part. The second phase consists of the estimation model
taking into account the criteria AIC
small errors and randomness of residuals. The third is validation: at this stage the 2015 collection whose figures already taken have been caused and as the screening of this year in contrast to ensure the quality of the model. The last phase is the prediction
in which case the collection is expected by 2016
taking a confidence interval of 80%. It should be noted that collection is made up the value to pay more interest on arrears and is expressed in thousands of pesos. The frequency of the time series analyzed bimonthly seasonal variation is a very important characteristic to be accounted for in a time series forecasting model. Traditionally
the role of autocorrelation function is used to identify if the process is stationary and then the partial autocorrelation function is used to detect the model. Once
guaranteed the stationarity of the process provides that the historical model behavior of the series is set in an . To validate if the selected model is appropriate
Box Ljung test
Shapiro Wilk test and integrated periodogram are used. Concluding that the residuals are not correlated
normally distributed and randomly.
description Con el fin de pronosticar el comportamiento del recaudo del impuesto de industria, comercio, avisos y tableros (ICA), para el año 2016, se plantea la metodología de modelos ARIMA, por su capacidad de predicción a corto plazo. Se definen cuatro fases: La primera es la identificación, describiendo el comportamiento del recaudo histórico y evaluando la posibilidad de convertir la serie estacionaria tanto en su parte regular como estacional. La segunda fase consiste en la estimación del modelo, teniendo en cuenta el criterio AIC, menores errores y aleatoriedad de los residuos. La tercera es la validación: en esta fase se toma el recaudo del año 2015 cuyas cifras ya han sido causadas y se contrasta la proyección referente a este año con el fin de garantizar la calidad del modelo. La última fase es la predicción, en cuyo caso se pronostica el recaudo año 2016, teniendo en cuenta un intervalo de confianza del 80%. Cabe anotar que el recaudo está constituido por el valor a pagar más los intereses de mora y se encuentra expresado en miles de millones de pesos. La frecuencia de la serie de tiempo analizada es bimestral y las variaciones estacionales es una característica importante que es tenida en cuenta en la serie de tiempo para predecir el modelo. Adicionalmente, se usa la función de autocorrelación para identificar si el proceso es estacionario y posteriormente se utiliza la función de autocorrelación parcial para detectar el modelo. Una vez garantizada la estacionariedad del proceso, se establece que el modelo que se ajusta al comportamiento histórico de la serie es un (0,1,1)(0,1,1)6. Para validar si el modelo seleccionado es adecuado, se usa: el test de Box Ljung, la prueba de Shapiro Wilk y el periodograma integrado. Concluyendo que los residuos no están autocorrelacionados, se distribuyen normal y aleatoriamente.
publishDate 2016
dc.date.available.none.fl_str_mv 2016
2024-03-03T22:50:00Z
dc.date.created.none.fl_str_mv 2016
dc.date.issued.none.fl_str_mv 2016
dc.date.accessioned.none.fl_str_mv 2024-03-03T22:50:00Z
dc.type.local.none.fl_str_mv Tesis/Trabajo de grado - Monografía - Pregrado
dc.type.hasversion.none.fl_str_mv http://purl.org/coar/resource_type/c_7a1f
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dc.identifier.uri.none.fl_str_mv https://noesis.uis.edu.co/handle/20.500.14071/35510
dc.identifier.instname.none.fl_str_mv Universidad Industrial de Santander
dc.identifier.reponame.none.fl_str_mv Universidad Industrial de Santander
dc.identifier.repourl.none.fl_str_mv https://noesis.uis.edu.co
url https://noesis.uis.edu.co/handle/20.500.14071/35510
https://noesis.uis.edu.co
identifier_str_mv Universidad Industrial de Santander
dc.language.iso.none.fl_str_mv spa
language spa
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dc.rights.uri.none.fl_str_mv http://creativecommons.org/licenses/by-nc/4.0
dc.rights.creativecommons.none.fl_str_mv Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
rights_invalid_str_mv Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
http://creativecommons.org/licenses/by/4.0/
http://creativecommons.org/licenses/by-nc/4.0
Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
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dc.publisher.none.fl_str_mv Universidad Industrial de Santander
dc.publisher.faculty.none.fl_str_mv Facultad de Ciencias
dc.publisher.program.none.fl_str_mv Especialización en Estadística
dc.publisher.school.none.fl_str_mv Escuela de Matemáticas
publisher.none.fl_str_mv Universidad Industrial de Santander
institution Universidad Industrial de Santander
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spelling Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)http://creativecommons.org/licenses/by/4.0/http://creativecommons.org/licenses/by-nc/4.0Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_abf2Orlandoni Merli, GiampaoloRosas Gualdron, Adriana Marcela2024-03-03T22:50:00Z20162024-03-03T22:50:00Z20162016https://noesis.uis.edu.co/handle/20.500.14071/35510Universidad Industrial de SantanderUniversidad Industrial de Santanderhttps://noesis.uis.edu.coCon el fin de pronosticar el comportamiento del recaudo del impuesto de industria, comercio, avisos y tableros (ICA), para el año 2016, se plantea la metodología de modelos ARIMA, por su capacidad de predicción a corto plazo. Se definen cuatro fases: La primera es la identificación, describiendo el comportamiento del recaudo histórico y evaluando la posibilidad de convertir la serie estacionaria tanto en su parte regular como estacional. La segunda fase consiste en la estimación del modelo, teniendo en cuenta el criterio AIC, menores errores y aleatoriedad de los residuos. La tercera es la validación: en esta fase se toma el recaudo del año 2015 cuyas cifras ya han sido causadas y se contrasta la proyección referente a este año con el fin de garantizar la calidad del modelo. La última fase es la predicción, en cuyo caso se pronostica el recaudo año 2016, teniendo en cuenta un intervalo de confianza del 80%. Cabe anotar que el recaudo está constituido por el valor a pagar más los intereses de mora y se encuentra expresado en miles de millones de pesos. La frecuencia de la serie de tiempo analizada es bimestral y las variaciones estacionales es una característica importante que es tenida en cuenta en la serie de tiempo para predecir el modelo. Adicionalmente, se usa la función de autocorrelación para identificar si el proceso es estacionario y posteriormente se utiliza la función de autocorrelación parcial para detectar el modelo. Una vez garantizada la estacionariedad del proceso, se establece que el modelo que se ajusta al comportamiento histórico de la serie es un (0,1,1)(0,1,1)6. Para validar si el modelo seleccionado es adecuado, se usa: el test de Box Ljung, la prueba de Shapiro Wilk y el periodograma integrado. Concluyendo que los residuos no están autocorrelacionados, se distribuyen normal y aleatoriamente.EspecializaciónEspecialista en EstadísticaTax collection forecasting on industry, commerce, boards and advertising: arima modeling application.application/pdfspaUniversidad Industrial de SantanderFacultad de CienciasEspecialización en EstadísticaEscuela de MatemáticasRecaudoImpuestoEstacionariedadEstacionalidadModelo Arima (Modelo Autoregresivo Integrado De Media Móvil)Proyección.In order to predict the behavior of tax collection on industrycommerceboards and advertising (ICA) for the year 2016the methodology of ARIMA models arises by their ability to predict short term. In order to achieve the goal there are four different phases: The first is identifyingdescribing the behavior of the historical collection and assess the possibility of converting the stationary series both seasonal and regular part. The second phase consists of the estimation modeltaking into account the criteria AICsmall errors and randomness of residuals. The third is validation: at this stage the 2015 collection whose figures already taken have been caused and as the screening of this year in contrast to ensure the quality of the model. The last phase is the predictionin which case the collection is expected by 2016taking a confidence interval of 80%. It should be noted that collection is made up the value to pay more interest on arrears and is expressed in thousands of pesos. The frequency of the time series analyzed bimonthly seasonal variation is a very important characteristic to be accounted for in a time series forecasting model. Traditionallythe role of autocorrelation function is used to identify if the process is stationary and then the partial autocorrelation function is used to detect the model. Onceguaranteed the stationarity of the process provides that the historical model behavior of the series is set in an . To validate if the selected model is appropriateBox Ljung testShapiro Wilk test and integrated periodogram are used. Concluding that the residuals are not correlatednormally distributed and randomly.Aplicación de modelos arima para la proyección del recaudo del impuesto de industria, comercio, avisos y tableros ICACollection, Tax, Stationarity, Seasonality, Arima (Autoregressive Integrated Moving Average Model), Projection.Tesis/Trabajo de grado - Monografía - Pregradohttp://purl.org/coar/resource_type/c_7a1fhttp://purl.org/coar/version/c_b1a7d7d4d402bcceORIGINALCarta de autorización.pdfapplication/pdf221440https://noesis.uis.edu.co/bitstreams/b0207d25-cd9f-4c22-84b3-14920a07c7e3/download0650451785015d2733afd109420c7089MD51Documento.pdfapplication/pdf1187349https://noesis.uis.edu.co/bitstreams/45e88c46-9c92-4dbf-ae32-c10ce53bdb43/downloadade736bad78754c806907373158e141bMD52Nota de proyecto.pdfapplication/pdf250597https://noesis.uis.edu.co/bitstreams/8ff14f44-9374-4c66-83a0-5ab750a49bf0/download047ee487a777d8e027abf782c101f566MD5320.500.14071/35510oai:noesis.uis.edu.co:20.500.14071/355102024-03-03 17:50:00.307http://creativecommons.org/licenses/by-nc/4.0http://creativecommons.org/licenses/by/4.0/open.accesshttps://noesis.uis.edu.coDSpace at UISnoesis@uis.edu.co