Penalised regressions vs. autoregressive moving average models for forecasting inflation

This paper relates seasonal autoregressive moving average (SARMA) models with linear regression. Based on this relation, the paper shows that penalised linear models may surpass the out-of-sample forecasting accuracy of the best SARMA models when forecasting inflation based on past values, due to pe...

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Autores:: Ospina-Holguín, Javier Humberto
Ospina-Holguín, Ana Milena

Tipo de recurso:: Article of journal

Fecha de publicación:: 2020

Institución:: Corporación Universidad de la Costa

Repositorio:: REDICUC - Repositorio CUC

Idioma:: eng

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dc.title.spa.fl_str_mv	Penalised regressions vs. autoregressive moving average models for forecasting inflation
dc.title.translated.eng.fl_str_mv	Regresiones penalizadas vs. modelos autorregresivos de media móvil para pronosticar la inflación
title	Penalised regressions vs. autoregressive moving average models for forecasting inflation
spellingShingle	Penalised regressions vs. autoregressive moving average models for forecasting inflation Ridge regression Penalised linear model ARMA SARMA Inflation forecasting Regresión de arista Modelo lineal penalizado ARMA SARMA Pronóstico de la inflación
title_short	Penalised regressions vs. autoregressive moving average models for forecasting inflation
title_full	Penalised regressions vs. autoregressive moving average models for forecasting inflation
title_fullStr	Penalised regressions vs. autoregressive moving average models for forecasting inflation
title_full_unstemmed	Penalised regressions vs. autoregressive moving average models for forecasting inflation
title_sort	Penalised regressions vs. autoregressive moving average models for forecasting inflation
dc.creator.fl_str_mv	Ospina-Holguín, Javier Humberto Ospina-Holguín, Ana Milena
dc.contributor.author.spa.fl_str_mv	Ospina-Holguín, Javier Humberto Ospina-Holguín, Ana Milena
dc.subject.eng.fl_str_mv	Ridge regression Penalised linear model ARMA SARMA Inflation forecasting
topic	Ridge regression Penalised linear model ARMA SARMA Inflation forecasting Regresión de arista Modelo lineal penalizado ARMA SARMA Pronóstico de la inflación
dc.subject.spa.fl_str_mv	Regresión de arista Modelo lineal penalizado ARMA SARMA Pronóstico de la inflación
description	This paper relates seasonal autoregressive moving average (SARMA) models with linear regression. Based on this relation, the paper shows that penalised linear models may surpass the out-of-sample forecasting accuracy of the best SARMA models when forecasting inflation based on past values, due to penalisation and cross-validation. The paper constructs a minimal working example using ridge regression to compare both of the competing approaches when forecasting the monthly inflation in 35 selected countries of the Organisation for Economic Co-operation and Development and in three groups of countries. The results empirically verify the hypothesis that penalised linear regression, and ridge regression in particular, can outperform the best standard SARMA models computed through a grid search when forecasting inflation. Thus, a new and effective technique for forecasting inflation based on past values is provided for use by financial analysts and investors. The results indicate that more attention should be given to machine learning techniques for time series forecasting of inflation, even as basic as penalised linear regressions, due to their superior empirical performance.
publishDate	2020
dc.date.accessioned.none.fl_str_mv	2020-01-01 00:00:00 2024-04-09T20:09:31Z
dc.date.available.none.fl_str_mv	2020-01-01 00:00:00 2024-04-09T20:09:31Z
dc.date.issued.none.fl_str_mv	2020-01-01
dc.type.spa.fl_str_mv	Artículo de revista
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dc.identifier.eissn.none.fl_str_mv	2382-3860
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dc.relation.references.eng.fl_str_mv	Anzola, C., Vargas, P. & Morales, A. (2019). Transición entre sistemas financieros bancarios y bursátiles. Una aproximación mediante modelo de Swithing Markov. Económicas CUC, 40(1), 123–144. https://doi.org/10.17981/econcuc.40.1.2019.08 Box, G. E. P. & Jenkin, G. M. (1976). Time series analysis, forecasting and control. San Francisco: Holden-Day. Burnham, K. P. & Anderson, D. R. (2004). Multimodel inference. Sociological Methods & Research, 33(2), 261–304. https://doi.org/10.1177/0049124104268644 Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427–431. https://doi.org/10.2307/2286348 Diebold, F. X. & Mariano, R. S. (2002). Comparing Predictive Accuracy. Journal of Business & Economic Statistics, 20(1), 134–144. https://doi.org/10.1198/073500102753410444 Faust, J. & Wright, J. H. (2013). Forecasting Inflation. In, G. Elliott & A. Timmermann (Eds.), Handbook of Economic Forecasting, Vol. 2. Part. A (pp. 2–56). Amsterdam: Elsevier. https://doi.org/10.1016/B978-0-444-53683-9.00001-3 Gil, J., Castellanos, D. & Gonzalez, D. (2019). Margen de intermediación y concentración bancaria en Colombia: un análisis para el periodo 2000-2017. Económicas CUC, 40(2), 9–30. https://doi.org/10.17981/econcuc.40.2.2019.01 Gómez, C., Sánchez, V. & Millán, E. (2019). Capitalismo y ética: una relación de tensiones. Económicas CUC, 40(2), 31–42. https://doi.org/10.17981/econcuc.40.2.2019.02 Gu, S., Kelly, B. T. & Xiu, D. (december, 2018). Empirical Asset Pricing Via Machine Learning [Paper 18-04]. 31st Australasian Finance and Banking Conference, AFBC, Sydney, Australia, 1–79. https://doi.org/10.2139/ssrn.3159577 Hoerl, A. E. & Kennard, R. W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. https://doi.org/10.2307/1267351 Hyndman, R. J. (july, 2013). Facts and fallacies of the AIC. [Online]. Available from https://robjhyndman.com/hyndsight/aic/ Hyndman, R. J. & Khandakar, Y. (2008). Automatic Time Series Forecasting: The forecast Package for R. Journal of Statistical Software, 27(1), 1–22. https://doi.org/10.18637/jss.v027.i03 Kvalseth, T. O. (1985). Cautionary Note about R 2. The American Statistician, 39(4), 279–285. https://doi.org/10.2307/2683704 MacKinnon, J. G. (1996). Numerical distribution functions for unit root and cointegration tests. Journal of Applied Econometrics, 11(6), 601–618. https://doi.org/10.1002/(SICI)1099-1255(199611)11:6<601::AID-JAE417>3.0.CO;2-T Mockus, J. (1989). Bayesian approach to global optimization. Dordrecht: Kluwer Academic Publishers. Mullainathan, S. & Spiess, J. (2017). Machine Learning: An Applied Econometric Approach. Journal of Economic Perspectives, 31(2), 87–106. https://doi.org/10.1257/jep.31.2.87 OECD. (2019). Inflation (CPI). [indicator]. https://doi.org/10.1787/eee82e6e-en Osborn, D. R., Chui, A. P. L., Smith, J. P. & Birchenhall, C. R. (2009). Seasonality and the order of integration for consumption. Oxford Bulletin of Economics and Statistics, 50(4), 361–377. https://doi.org/10.1111/j.1468-0084.1988.mp50004002.x Quinn, T., Kenny, G. & Meyler, A. (1999). Inflation analysis: An overview. [MPRA Paper No. 11361]. Munich: UTC. Retrieved from https://mpra.ub.uni-muenchen.de/11361/1/MPRA_paper_11361.pdf Santosa, F. & Symes, W. W. (1986). Linear Inversion of Band-Limited Reflection Seismograms. SIAM Journal on Scientific and Statistical Computing, 7(4), 1307–1330. https://doi.org/10.1137/0907087 Tibshirani, R. (1996). Regression Shrinkage and Selection Via the Lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267–288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x Tikhonov, A. N. & Arsenin, V. Y. (1977). Solution of ill-posed problems. Washington: Winston & Sons. Zou, H. & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301–320. https://doi.org/10.1111/j.1467-9868.2005.00503.x
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dc.relation.citationedition.spa.fl_str_mv	Núm. 1 , Año 2020
dc.rights.eng.fl_str_mv	Javier Humberto Ospina-Holguín, Ana Milena Ospina-Holguín - 2020
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spelling	Ospina-Holguín, Javier HumbertoOspina-Holguín, Ana Milena2020-01-01 00:00:002024-04-09T20:09:31Z2020-01-01 00:00:002024-04-09T20:09:31Z2020-01-010120-3932https://hdl.handle.net/11323/11900https://doi.org/10.17981/econcuc.41.1.2020.Econ.310.17981/econcuc.41.1.2020.Econ.32382-3860This paper relates seasonal autoregressive moving average (SARMA) models with linear regression. Based on this relation, the paper shows that penalised linear models may surpass the out-of-sample forecasting accuracy of the best SARMA models when forecasting inflation based on past values, due to penalisation and cross-validation. The paper constructs a minimal working example using ridge regression to compare both of the competing approaches when forecasting the monthly inflation in 35 selected countries of the Organisation for Economic Co-operation and Development and in three groups of countries. The results empirically verify the hypothesis that penalised linear regression, and ridge regression in particular, can outperform the best standard SARMA models computed through a grid search when forecasting inflation. Thus, a new and effective technique for forecasting inflation based on past values is provided for use by financial analysts and investors. The results indicate that more attention should be given to machine learning techniques for time series forecasting of inflation, even as basic as penalised linear regressions, due to their superior empirical performance.Este artículo relaciona los modelos autorregresivos estacionales de media móvil (SARMA) con la regresión lineal. Sobre la base de esta relación, el documento muestra que los modelos lineales penalizados pueden superar la precisión del pronóstico fuera de la muestra de los mejores modelos SARMA al pronosticar la inflación en función de valores pasados, debido a la penalización y a la validación cruzada. El artículo construye un ejemplo funcional mínimo utilizando la regresión de arista para comparar ambos enfoques que compiten al pronosticar la inflación mensual en 35 países seleccionados de la Organización para la Cooperación y el Desarrollo Económico y en tres grupos de países. Los resultados verifican empíricamente la hipótesis de que la regresión lineal penalizada, y la regresión de arista en particular, puede superar a los mejores modelos estándar SARMA calculados a través de una búsqueda de cuadrícula cuando se pronostica la inflación. Así, se proporciona una técnica nueva y efectiva para pronosticar la inflación basada en valores pasados para el uso de analistas financieros e inversores. Los resultados indican que se debe prestar más atención a las técnicas de aprendizaje automático para el pronóstico de series de tiempo de la inflación, incluso tan básicas como las regresiones lineales penalizadas, debido a su rendimiento empírico superior.application/pdftext/htmlapplication/xmlengUniversidad de la CostaJavier Humberto Ospina-Holguín, Ana Milena Ospina-Holguín - 2020https://creativecommons.org/licenses/by-nc-nd/4.0info:eu-repo/semantics/openAccessEsta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.http://purl.org/coar/access_right/c_abf2https://revistascientificas.cuc.edu.co/economicascuc/article/view/2657Ridge regressionPenalised linear modelARMASARMAInflation forecastingRegresión de aristaModelo lineal penalizadoARMASARMAPronóstico de la inflaciónPenalised regressions vs. autoregressive moving average models for forecasting inflationRegresiones penalizadas vs. modelos autorregresivos de media móvil para pronosticar la inflaciónArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articleJournal articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Económicas CUCAnzola, C., Vargas, P. & Morales, A. (2019). Transición entre sistemas financieros bancarios y bursátiles. Una aproximación mediante modelo de Swithing Markov. Económicas CUC, 40(1), 123–144. https://doi.org/10.17981/econcuc.40.1.2019.08Box, G. E. P. & Jenkin, G. M. (1976). Time series analysis, forecasting and control. San Francisco: Holden-Day.Burnham, K. P. & Anderson, D. R. (2004). Multimodel inference. Sociological Methods & Research, 33(2), 261–304. https://doi.org/10.1177/0049124104268644Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427–431. https://doi.org/10.2307/2286348Diebold, F. X. & Mariano, R. S. (2002). Comparing Predictive Accuracy. Journal of Business & Economic Statistics, 20(1), 134–144. https://doi.org/10.1198/073500102753410444Faust, J. & Wright, J. H. (2013). Forecasting Inflation. In, G. Elliott & A. Timmermann (Eds.), Handbook of Economic Forecasting, Vol. 2. Part. A (pp. 2–56). Amsterdam: Elsevier. https://doi.org/10.1016/B978-0-444-53683-9.00001-3Gil, J., Castellanos, D. & Gonzalez, D. (2019). Margen de intermediación y concentración bancaria en Colombia: un análisis para el periodo 2000-2017. Económicas CUC, 40(2), 9–30. https://doi.org/10.17981/econcuc.40.2.2019.01Gómez, C., Sánchez, V. & Millán, E. (2019). Capitalismo y ética: una relación de tensiones. Económicas CUC, 40(2), 31–42. https://doi.org/10.17981/econcuc.40.2.2019.02Gu, S., Kelly, B. T. & Xiu, D. (december, 2018). Empirical Asset Pricing Via Machine Learning [Paper 18-04]. 31st Australasian Finance and Banking Conference, AFBC, Sydney, Australia, 1–79. https://doi.org/10.2139/ssrn.3159577Hoerl, A. E. & Kennard, R. W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. https://doi.org/10.2307/1267351Hyndman, R. J. (july, 2013). Facts and fallacies of the AIC. [Online]. Available from https://robjhyndman.com/hyndsight/aic/Hyndman, R. J. & Khandakar, Y. (2008). Automatic Time Series Forecasting: The forecast Package for R. Journal of Statistical Software, 27(1), 1–22. https://doi.org/10.18637/jss.v027.i03Kvalseth, T. O. (1985). Cautionary Note about R 2. The American Statistician, 39(4), 279–285. https://doi.org/10.2307/2683704MacKinnon, J. G. (1996). Numerical distribution functions for unit root and cointegration tests. Journal of Applied Econometrics, 11(6), 601–618. https://doi.org/10.1002/(SICI)1099-1255(199611)11:6<601::AID-JAE417>3.0.CO;2-TMockus, J. (1989). Bayesian approach to global optimization. Dordrecht: Kluwer Academic Publishers.Mullainathan, S. & Spiess, J. (2017). Machine Learning: An Applied Econometric Approach. Journal of Economic Perspectives, 31(2), 87–106. https://doi.org/10.1257/jep.31.2.87OECD. (2019). Inflation (CPI). [indicator]. https://doi.org/10.1787/eee82e6e-enOsborn, D. R., Chui, A. P. L., Smith, J. P. & Birchenhall, C. R. (2009). Seasonality and the order of integration for consumption. Oxford Bulletin of Economics and Statistics, 50(4), 361–377. https://doi.org/10.1111/j.1468-0084.1988.mp50004002.xQuinn, T., Kenny, G. & Meyler, A. (1999). Inflation analysis: An overview. [MPRA Paper No. 11361]. Munich: UTC. Retrieved from https://mpra.ub.uni-muenchen.de/11361/1/MPRA_paper_11361.pdfSantosa, F. & Symes, W. W. (1986). Linear Inversion of Band-Limited Reflection Seismograms. SIAM Journal on Scientific and Statistical Computing, 7(4), 1307–1330. https://doi.org/10.1137/0907087Tibshirani, R. (1996). Regression Shrinkage and Selection Via the Lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267–288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.xTikhonov, A. N. & Arsenin, V. Y. (1977). Solution of ill-posed problems. Washington: Winston & Sons.Zou, H. & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301–320. https://doi.org/10.1111/j.1467-9868.2005.00503.x8065141https://revistascientificas.cuc.edu.co/economicascuc/article/download/2657/2824https://revistascientificas.cuc.edu.co/economicascuc/article/download/2657/2825https://revistascientificas.cuc.edu.co/economicascuc/article/download/2657/2826Núm. 1 , Año 2020PublicationOREORE.xmltext/xml2623https://repositorio.cuc.edu.co/bitstreams/34dfd2e9-c1ec-4494-89ca-f63933a9656e/downloadad9b9ca244e3bc9ec1fc3c4ea8fb0efdMD5111323/11900oai:repositorio.cuc.edu.co:11323/119002024-09-17 11:08:06.819https://creativecommons.org/licenses/by-nc-nd/4.0Javier Humberto Ospina-Holguín, Ana Milena Ospina-Holguín - 2020metadata.onlyhttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.co

Penalised regressions vs. autoregressive moving average models for forecasting inflation

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