Two-regime functional threshold autoregressive model: an empirical approach
Ilustraciones y tablas
- Autores:
-
Coba Puerto, Juan Eduardo
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/80184
- Palabra clave:
- 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas
Time-series analysis
Regression analysis
Statistical information
Análisis de series de tiempo
Análisis de regresión
Información estadística
TAR
TAR Model
Functional data analysis
Functional threshold autoregression
Functional time series
Modelo TAR
Análisis de datos funcionales
Modeloautoregressivo funcional con umbrales
Series de tiempo funcionales
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
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| dc.title.eng.fl_str_mv |
Two-regime functional threshold autoregressive model: an empirical approach |
| dc.title.translated.spa.fl_str_mv |
Modelo autorregressivo funcional con umbrales: una aproximación empírica |
| title |
Two-regime functional threshold autoregressive model: an empirical approach |
| spellingShingle |
Two-regime functional threshold autoregressive model: an empirical approach 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas Time-series analysis Regression analysis Statistical information Análisis de series de tiempo Análisis de regresión Información estadística TAR TAR Model Functional data analysis Functional threshold autoregression Functional time series Modelo TAR Análisis de datos funcionales Modeloautoregressivo funcional con umbrales Series de tiempo funcionales |
| title_short |
Two-regime functional threshold autoregressive model: an empirical approach |
| title_full |
Two-regime functional threshold autoregressive model: an empirical approach |
| title_fullStr |
Two-regime functional threshold autoregressive model: an empirical approach |
| title_full_unstemmed |
Two-regime functional threshold autoregressive model: an empirical approach |
| title_sort |
Two-regime functional threshold autoregressive model: an empirical approach |
| dc.creator.fl_str_mv |
Coba Puerto, Juan Eduardo |
| dc.contributor.advisor.none.fl_str_mv |
Calderón Villanueva, Sergio Alejandro Guevara Gonzalez, Rubén Darío |
| dc.contributor.author.none.fl_str_mv |
Coba Puerto, Juan Eduardo |
| dc.subject.ddc.spa.fl_str_mv |
510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas |
| topic |
510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas Time-series analysis Regression analysis Statistical information Análisis de series de tiempo Análisis de regresión Información estadística TAR TAR Model Functional data analysis Functional threshold autoregression Functional time series Modelo TAR Análisis de datos funcionales Modeloautoregressivo funcional con umbrales Series de tiempo funcionales |
| dc.subject.lemb.eng.fl_str_mv |
Time-series analysis Regression analysis Statistical information |
| dc.subject.lemb.spa.fl_str_mv |
Análisis de series de tiempo Análisis de regresión Información estadística |
| dc.subject.proposal.eng.fl_str_mv |
TAR TAR Model Functional data analysis Functional threshold autoregression Functional time series |
| dc.subject.proposal.spa.fl_str_mv |
Modelo TAR Análisis de datos funcionales Modeloautoregressivo funcional con umbrales Series de tiempo funcionales |
| description |
Ilustraciones y tablas |
| publishDate |
2021 |
| dc.date.accessioned.none.fl_str_mv |
2021-09-14T15:34:32Z |
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2021-09-14T15:34:32Z |
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2021 |
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Trabajo de grado - Maestría |
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info:eu-repo/semantics/masterThesis |
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https://repositorio.unal.edu.co/handle/unal/80184 |
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Universidad Nacional de Colombia |
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Repositorio Institucional Universidad Nacional de Colombia |
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https://repositorio.unal.edu.co/ |
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https://repositorio.unal.edu.co/handle/unal/80184 https://repositorio.unal.edu.co/ |
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Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
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eng |
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eng |
| dc.relation.references.none.fl_str_mv |
Aue, A., Van Delft, A., et al. (2020). Testing for stationarity of functional time series in the frequency domain. Annals of Statistics, 48(5):2505–2547. Chan, K.-S. (1993). Consistency and limiting distribution of the least squares estimator of a threshold autoregressive model. The annals of statistics, 21(1):520–533. Didericksen, D., Kokoszka, P., and Zhang, X. (2012). Empirical properties of forecasts with the functional autoregressive model. Computational Statistics, 27(2):285–298. Fan, J. and Yao, Q. (2008). Nonlinear time series: nonparametric and parametric methods. Springer Science & Business Media. Gabrys, R., Horváth, L., and Kokoszka, P. (2010). Tests for error correlation in the functional linear model. Journal of the American Statistical Association, 105(491):1113–1125. Grynkiv, G. and Stentoft, L. (2018). Stationary threshold vector autoregressive models. Journal of Risk and Financial Management, 11(3):45. Hansen, B. E. (2011). Threshold autoregression in economics. Statistics and its Interface, 4(2):123–127. Harezlak, J., Ruppert, D., and Wand, M. P. (2018). Semiparametric regression with R. Springer. HO, L.-C. and Huang, C.-S. (2016). Nonlinear relationships between oil price and stock index - evidence from brazil, russia, india and china. Romanian Journal of Economic Forecasting, XIX(3):116–126. Horváth, L. and Kokoszka, P. (2012). Inference for functional data with applications, volume 200. Springer Science & Business Media. Horváth, L., Kokoszka, P., and Rice, G. (2014). Testing stationarity of functional time series. Journal of Econometrics, 179(1):66–82. Hubrich, K. and Teräsvirta, T. (2013). Thresholds and Smooth Transitions in Vector Autoregressive Models. VAR Models in Macroeconomics–New Developments and Applications: Essays in Honor of Christopher A. Sims, pages 273–326. James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An introduction to statistical learning, volume 112. Springer. Kokoszka, P. (2012). Dependent functional data. ISRN Probability and Statistics, 2012. Kokoszka, P. and Reimherr, M. (2017). Introduction to functional data analysis. CRC Press. Kokoszka, P. and Zhang, X. (2012). Functional prediction of intraday cumulative returns. Statistical Modelling, 12(4):377–398. Kwiatkowski, D., Phillips, P. C., Schmidt, P., and Shin, Y. (1992). 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? Journal of Econometrics, 54(1-3):159–178. Ramsay, J., Hooker, G., and Graves, S. (2009). Functional Data Analysis with R and MATLAB. Springer. Ramsay, J. O. and Silverman, B. (2008). Functional data analysis. ˙Internet Adresi: http. Shang, H. L. (2017). Forecasting intraday s&p 500 index returns: A functional time series approach. Journal of forecasting, 36(7):741–755. Tong, H. and Lim, K. (1980). Threshold autoregression, limit cycles and cyclical data. Journal of the Royal Statistical Society. Series B (Methodological), 42(3):245–292. Tsay, R. S. (1998). Testing and modeling multivariate threshold models. Journal of the American Statistical Association, 93(443):1188–1202. Tsay, R. S. (2012). Nonlinearity and nonlinear econometric models in finance. Encyclopedia of Financial Models. Tsay, R. S. and Chen, R. (2019). Nonlinear time series analysis, volume 891. John Wiley & Sons. Wei, T. (2015). Time series in functional data analysis |
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Atribución-NoComercial 4.0 Internacional |
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xii, 54 páginas |
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Universidad Nacional de Colombia |
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Bogotá - Ciencias - Maestría en Ciencias - Estadística |
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Departamento de Estadística |
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Facultad de Ciencias |
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Bogotá, Colombia |
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Universidad Nacional de Colombia - Sede Bogotá |
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Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Calderón Villanueva, Sergio Alejandro4435821363acfcc5a0b97c50464db9d4Guevara Gonzalez, Rubén Darío1fbbedf734bb84fd0ff11d3d67ec403d600Coba Puerto, Juan Eduardo125524c7c287eea8d14ce5ede4264f2a2021-09-14T15:34:32Z2021-09-14T15:34:32Z2021https://repositorio.unal.edu.co/handle/unal/80184Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/Ilustraciones y tablasIt is known in the literature that economic and financial time series, such as stock returns or the exchange rate, present non-linear dynamics like regime switching, time-varying volatility, or volatility clusters, which should be modeled by using the appropriate non-linear models. Given that financial time series are often of high frequency, it is possible to split the series into intervals and treat each interval as a unique functional observational unit. In this work we introduce and explore, via simulation, a two-regime Functional Threshold Autoregressive model of order one, FTAR(2, 1, 1), as an extension of the univariate TAR(1) model, allowing for a discrete regime switching specification in functional time series governed by a scalar threshold process.Es bien sabido que las series de tiempo económicas y financieras, como los retornos de activos financieros o la tasa de cambio, presentan dinámicas no-lineales como cambios de régimen o volatilidad. Estos comportamientos pueden ser tenidos en cuenta utilizando modelos no-lineales. Adicionalmente, teniendo en cuenta que las series de tiempo financieras suelen ser de alta frecuencia, es posible dividir la serie en intervalos y hacer uso de las técnicas de Análisis de Datos Funcionales. En este trabajo se presenta y explora, por medio de simulaciones, el Modelo Autorregressivo Funcional con Umbrales, para el caso en que se tienen dos regímenes, cada uno de primer orden. Este modelo es una extensión del modelo TAR(1), de modo que se pueden modelar series de tiempo funcionales con cambios de régimen gobernados por un proceso de umbrales escalar. (Texto tomado de la fuente).MaestríaMagíster en Ciencias - EstadísticaSeries de Tiempo Funcionalesxii, 54 páginasapplication/pdfengUniversidad Nacional de ColombiaBogotá - Ciencias - Maestría en Ciencias - EstadísticaDepartamento de EstadísticaFacultad de CienciasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá510 - Matemáticas::519 - Probabilidades y matemáticas aplicadasTime-series analysisRegression analysisStatistical informationAnálisis de series de tiempoAnálisis de regresiónInformación estadísticaTARTAR ModelFunctional data analysisFunctional threshold autoregressionFunctional time seriesModelo TARAnálisis de datos funcionalesModeloautoregressivo funcional con umbralesSeries de tiempo funcionalesTwo-regime functional threshold autoregressive model: an empirical approachModelo autorregressivo funcional con umbrales: una aproximación empíricaTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAue, A., Van Delft, A., et al. (2020). Testing for stationarity of functional time series in the frequency domain. Annals of Statistics, 48(5):2505–2547.Chan, K.-S. (1993). Consistency and limiting distribution of the least squares estimator of a threshold autoregressive model. The annals of statistics, 21(1):520–533.Didericksen, D., Kokoszka, P., and Zhang, X. (2012). Empirical properties of forecasts with the functional autoregressive model. Computational Statistics, 27(2):285–298.Fan, J. and Yao, Q. (2008). Nonlinear time series: nonparametric and parametric methods. Springer Science & Business Media.Gabrys, R., Horváth, L., and Kokoszka, P. (2010). Tests for error correlation in the functional linear model. Journal of the American Statistical Association, 105(491):1113–1125.Grynkiv, G. and Stentoft, L. (2018). Stationary threshold vector autoregressive models.Journal of Risk and Financial Management, 11(3):45.Hansen, B. E. (2011). Threshold autoregression in economics. Statistics and its Interface, 4(2):123–127.Harezlak, J., Ruppert, D., and Wand, M. P. (2018). Semiparametric regression with R. Springer.HO, L.-C. and Huang, C.-S. (2016). Nonlinear relationships between oil price and stock index - evidence from brazil, russia, india and china. Romanian Journal of Economic Forecasting, XIX(3):116–126.Horváth, L. and Kokoszka, P. (2012). Inference for functional data with applications, volume 200. Springer Science & Business Media.Horváth, L., Kokoszka, P., and Rice, G. (2014). Testing stationarity of functional time series. Journal of Econometrics, 179(1):66–82.Hubrich, K. and Teräsvirta, T. (2013). Thresholds and Smooth Transitions in Vector Autoregressive Models. VAR Models in Macroeconomics–New Developments and Applications: Essays in Honor of Christopher A. Sims, pages 273–326.James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An introduction to statistical learning, volume 112. Springer.Kokoszka, P. (2012). Dependent functional data. ISRN Probability and Statistics, 2012.Kokoszka, P. and Reimherr, M. (2017). Introduction to functional data analysis. CRC Press.Kokoszka, P. and Zhang, X. (2012). Functional prediction of intraday cumulative returns. Statistical Modelling, 12(4):377–398.Kwiatkowski, D., Phillips, P. C., Schmidt, P., and Shin, Y. (1992). 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? Journal of Econometrics, 54(1-3):159–178.Ramsay, J., Hooker, G., and Graves, S. (2009). Functional Data Analysis with R and MATLAB. Springer.Ramsay, J. O. and Silverman, B. (2008). Functional data analysis. ˙Internet Adresi: http.Shang, H. L. (2017). Forecasting intraday s&p 500 index returns: A functional time series approach. Journal of forecasting, 36(7):741–755.Tong, H. and Lim, K. (1980). Threshold autoregression, limit cycles and cyclical data. Journal of the Royal Statistical Society. Series B (Methodological), 42(3):245–292.Tsay, R. S. (1998). Testing and modeling multivariate threshold models. Journal of the American Statistical Association, 93(443):1188–1202.Tsay, R. S. (2012). Nonlinearity and nonlinear econometric models in finance. Encyclopedia of Financial Models.Tsay, R. S. and Chen, R. (2019). Nonlinear time series analysis, volume 891. John Wiley & Sons.Wei, T. (2015). Time series in functional data analysisEstudiantesInvestigadoresMaestrosPúblico generalLICENSElicense.txtlicense.txttext/plain; charset=utf-83964https://repositorio.unal.edu.co/bitstream/unal/80184/1/license.txtcccfe52f796b7c63423298c2d3365fc6MD51ORIGINAL1019127061.2021.pdf1019127061.2021.pdfTesis de Maestría en Ciencias - Estadísticaapplication/pdf9746548https://repositorio.unal.edu.co/bitstream/unal/80184/2/1019127061.2021.pdf91e5035b1b9d300a174759cf1b9cd0eaMD52THUMBNAIL1019127061.2021.pdf.jpg1019127061.2021.pdf.jpgGenerated Thumbnailimage/jpeg4293https://repositorio.unal.edu.co/bitstream/unal/80184/3/1019127061.2021.pdf.jpge30c328cb078d3fd9dcb430d83a8de2dMD53unal/80184oai:repositorio.unal.edu.co:unal/801842023-07-27 23:04:15.298Repositorio Institucional Universidad Nacional de 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