Una aplicación del Modelo Panel - VAR en Tiempo Continuo en Economía
ilustraciones
- Autores:
-
Luna Espíndola, Luis Alfonso
- Tipo de recurso:
- Fecha de publicación:
- 2023
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/83988
- Palabra clave:
- C1 Métodos y Metodología Econométrica y Estadística: General
C32 - Modelos de series temporales
C33 - Modelos con datos de panel
Series de tiempo
Tiempo Continuo
Econometría
VAR
PVAR
- Rights
- openAccess
- License
- Reconocimiento 4.0 Internacional
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| dc.title.spa.fl_str_mv |
Una aplicación del Modelo Panel - VAR en Tiempo Continuo en Economía |
| dc.title.translated.eng.fl_str_mv |
A Continuous-Time Application of the Panel-VAR Model in Economics |
| title |
Una aplicación del Modelo Panel - VAR en Tiempo Continuo en Economía |
| spellingShingle |
Una aplicación del Modelo Panel - VAR en Tiempo Continuo en Economía C1 Métodos y Metodología Econométrica y Estadística: General C32 - Modelos de series temporales C33 - Modelos con datos de panel Series de tiempo Tiempo Continuo Econometría VAR PVAR |
| title_short |
Una aplicación del Modelo Panel - VAR en Tiempo Continuo en Economía |
| title_full |
Una aplicación del Modelo Panel - VAR en Tiempo Continuo en Economía |
| title_fullStr |
Una aplicación del Modelo Panel - VAR en Tiempo Continuo en Economía |
| title_full_unstemmed |
Una aplicación del Modelo Panel - VAR en Tiempo Continuo en Economía |
| title_sort |
Una aplicación del Modelo Panel - VAR en Tiempo Continuo en Economía |
| dc.creator.fl_str_mv |
Luna Espíndola, Luis Alfonso |
| dc.contributor.advisor.none.fl_str_mv |
Hoyos Gómez, Nancy Milena |
| dc.contributor.author.none.fl_str_mv |
Luna Espíndola, Luis Alfonso |
| dc.subject.jel.spa.fl_str_mv |
C1 Métodos y Metodología Econométrica y Estadística: General C32 - Modelos de series temporales C33 - Modelos con datos de panel |
| topic |
C1 Métodos y Metodología Econométrica y Estadística: General C32 - Modelos de series temporales C33 - Modelos con datos de panel Series de tiempo Tiempo Continuo Econometría VAR PVAR |
| dc.subject.proposal.spa.fl_str_mv |
Series de tiempo Tiempo Continuo Econometría VAR PVAR |
| description |
ilustraciones |
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2023 |
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2023-06-07T14:37:08Z |
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2023-06-07T14:37:08Z |
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2023 |
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Trabajo de grado - Maestría |
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info:eu-repo/semantics/masterThesis |
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Universidad Nacional de Colombia |
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Repositorio Institucional Universidad Nacional de Colombia |
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[Bergstrom, 1984] Bergstrom, A. (1984). Chapter 20 Continuous time stochastic models and issues of aggregation over time. In Handbook of Econometrics, volume 2, pages 1145–1212. Elsevier. [Bergstrom, 1983] Bergstrom, A. R. (1983). Gaussian Estimation of Structural Parameters in Higher Order Continuous Time Dynamic Models. Econometrica, 51(1):117 [Bergstrom, 1996] Bergstrom, A. R. (1996). Survey of continuous time econometrics. In Barnett, W. A., Gandolfo, G., and Hillinger, C., editors, Dynamic Disequilibrium Modeling, pages 3–25. Cambridge University Press. [Canova, 2005] Canova, F. (2005). The transmission of US shocks to Latin America. Journal of Applied Econometrics, 20(2):229–251. [Canova and Ciccarelli, 2013] Canova, F. and Ciccarelli, M. (2013). Panel vector autore- gressive models: a survey. ECB Working Paper 1507, European Central Bank (ECB), Frankfurt a. M. [Chambers et al., 2018] Chambers, M. J., McCrorie, J. R., and Thornton, M. A. (2018). Continuous Time Modelling Based on an Exact Discrete Time Representation. In van Montfort, K., Oud, J. H. L., and Voelkle, M. C., editors, Continuous Time Modeling in the Behavioral and Related Sciences, pages 317–357. Springer International Publishing, Cham. [Christiano and Eichenbaum, 1987] Christiano, L. J. and Eichenbaum, M. (1987). Temporal aggregation and structural inference in macroeconomics. Carnegie-Rochester Conference Series on Public Policy, 26:63–130. [C ́espedes and Velasco, 2012] C ́espedes, L. F. and Velasco, A. (2012). Macroeconomic Per- formance During Commodity Price Booms and Busts. Technical Report w18569, National Bureau of Economic Research, Cambridge, MA. [Gondo and Pérez, 2018] Gondo, R. and P ́erez, F. (2018). The Transmission of Exogenous Commodity and Oil Prices shocks to Latin America: A Panel VAR approach. Working Paper series Banco Central de Reserva del Per ́u, DT. N°. 2018(012). [Gruss, 2014] Gruss, B. (2014). After the Boom–Commodity Prices and Economic Growth in Latin America and the Caribbean. IMF Working Paper, 14(154) [Hansen and Sargent, 1983] Hansen, L. P. and Sargent, T. J. (1983). The dimensionality of the aliasing problem in models with rational spectral densities. 51(2):377 [Jewitt and Roderick McCrorie, 2005] Jewitt, G. and Roderick McCrorie, J. (2005). Com- puting estimates of continuous time macroeconometric models on the basis of discrete data. Computational Statistics & Data Analysis, 49(2):397–416. [Koop and Korobilis, 2014] Koop, G. and Korobilis, D. (2014). Model Uncertainty in Panel Vector Autoregressive Models. SSRN Electronic Journal. [McCrorie, 2003] McCrorie, J. R. (2003). The Problem of Aliasing in Identifying Finite Parameter Continuous Time Stochastic Models. Acta Applicandae Mathematicae, 79(1/2):9– 16. [McCrorie and Chambers, 2006] McCrorie, J. R. and Chambers, M. J. (2006). Granger cau- sality and the sampling of economic processes. Journal of Econometrics, 132(2):311–336. [Medina, 2010] Medina, L. (2010). A Commodity Curse? The Dynamic Effects of Commo- dity Prices on Fiscal Performance in Latin America. MPRA Paper, 21690. [Phillips, 1973] Phillips, P. (1973). The problem of identification in finite parameter conti- nuous time models. Journal of Econometrics, 1(4):351–362. [Ryan et al., 2018] Ryan, O., Kuiper, R. M., and Hamaker, E. L. (2018). A Continuous-Time Approach to Intensive Longitudinal Data: What, Why, and How? In van Montfort, K., Oud, J. H. L., and Voelkle, M. C., editors, Continuous Time Modeling in the Behavioral and Related Sciences, pages 27–54. Springer International Publishing, Cham. [Van Loan, 1978] Van Loan, C. (1978). Computing integrals involving the matrix exponential. IEEE Transactions on Automatic Control, 23(3):395–404. [Voelkle and Oud, 2013] Voelkle, M. C. and Oud, J. H. L. (2013). Continuous time modelling with individually varying time intervals for oscillating and non-oscillating processes: Continuous time modelling. British Journal of Mathematical and Statistical Psychology, 66(1):103–126. [Voelkle et al., 2012] Voelkle, M. C., Oud, J. H. L., Davidov, E., and Schmidt, P. (2012). An SEM approach to continuous time modeling of panel data: Relating authoritarianism and anomia: Correction to Voelkle, Oud, Davidov, and Schmidt (2012). Psychological Methods, 17(3):384–384. |
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v, 40 páginas |
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Universidad Nacional de Colombia |
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Bogotá - Ciencias - Maestría en Ciencias - 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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Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Hoyos Gómez, Nancy Milena12999640324d4aba16dd48e0d34cd393Luna Espíndola, Luis Alfonsoaf822a217066fa069dc4c0f3ff959bfb2023-06-07T14:37:08Z2023-06-07T14:37:08Z2023https://repositorio.unal.edu.co/handle/unal/83988Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustracionesSe presenta la metodología para estimar modelos Panel VAR (PVAR) en tiempo continuo. Esta técnica permite estimar e interpretar modelos independientemente del intervalo en el que son tomadas las observaciones y permite hacer análisis en cualquier periodo de tiempo. Se ilustra con un Panel VAR en tiempo continuo de la Masa Monetaria (M1) y las Reservas Internacionales (IR) de dos países de la Alianza del Pacífico: Colombia y Chile, usando como variable exógena común al precio internacional del petróleo (WTI). (texto tomado de la fuente)The methodology for estimating Continuous Time Panel VAR (PVAR) models is presented. This technique allows for estimating and interpreting models regardless of the interval at which observations are taken and enables analysis in any time period. It is illustrated with a Continuous Time Panel VAR of the Money Supply (M1) and International Reserves (IR) of two countries in the Pacific Alliance: Colombia and Chile, using the international oil price (WTI) as a common exogenous variableMaestríaMagíster en Ciencias - EstadísticaSeries de tiempov, 40 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias - Maestría en Ciencias - EstadísticaFacultad de CienciasBogotá,ColombiaUniversidad Nacional de Colombia - Sede BogotáUna aplicación del Modelo Panel - VAR en Tiempo Continuo en EconomíaA Continuous-Time Application of the Panel-VAR Model in EconomicsTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TM[Bergstrom, 1984] Bergstrom, A. (1984). Chapter 20 Continuous time stochastic models and issues of aggregation over time. In Handbook of Econometrics, volume 2, pages 1145–1212. Elsevier.[Bergstrom, 1983] Bergstrom, A. R. (1983). Gaussian Estimation of Structural Parameters in Higher Order Continuous Time Dynamic Models. Econometrica, 51(1):117[Bergstrom, 1996] Bergstrom, A. R. (1996). Survey of continuous time econometrics. In Barnett, W. A., Gandolfo, G., and Hillinger, C., editors, Dynamic Disequilibrium Modeling, pages 3–25. Cambridge University Press.[Canova, 2005] Canova, F. (2005). The transmission of US shocks to Latin America. Journal of Applied Econometrics, 20(2):229–251.[Canova and Ciccarelli, 2013] Canova, F. and Ciccarelli, M. (2013). Panel vector autore- gressive models: a survey. ECB Working Paper 1507, European Central Bank (ECB), Frankfurt a. M.[Chambers et al., 2018] Chambers, M. J., McCrorie, J. R., and Thornton, M. A. (2018). Continuous Time Modelling Based on an Exact Discrete Time Representation. In van Montfort, K., Oud, J. H. L., and Voelkle, M. C., editors, Continuous Time Modeling in the Behavioral and Related Sciences, pages 317–357. Springer International Publishing, Cham.[Christiano and Eichenbaum, 1987] Christiano, L. J. and Eichenbaum, M. (1987). Temporal aggregation and structural inference in macroeconomics. Carnegie-Rochester Conference Series on Public Policy, 26:63–130.[C ́espedes and Velasco, 2012] C ́espedes, L. F. and Velasco, A. (2012). Macroeconomic Per- formance During Commodity Price Booms and Busts. Technical Report w18569, National Bureau of Economic Research, Cambridge, MA.[Gondo and Pérez, 2018] Gondo, R. and P ́erez, F. (2018). The Transmission of Exogenous Commodity and Oil Prices shocks to Latin America: A Panel VAR approach. Working Paper series Banco Central de Reserva del Per ́u, DT. N°. 2018(012).[Gruss, 2014] Gruss, B. (2014). After the Boom–Commodity Prices and Economic Growth in Latin America and the Caribbean. IMF Working Paper, 14(154)[Hansen and Sargent, 1983] Hansen, L. P. and Sargent, T. J. (1983). The dimensionality of the aliasing problem in models with rational spectral densities. 51(2):377[Jewitt and Roderick McCrorie, 2005] Jewitt, G. and Roderick McCrorie, J. (2005). Com- puting estimates of continuous time macroeconometric models on the basis of discrete data. Computational Statistics & Data Analysis, 49(2):397–416.[Koop and Korobilis, 2014] Koop, G. and Korobilis, D. (2014). Model Uncertainty in Panel Vector Autoregressive Models. SSRN Electronic Journal.[McCrorie, 2003] McCrorie, J. R. (2003). The Problem of Aliasing in Identifying Finite Parameter Continuous Time Stochastic Models. Acta Applicandae Mathematicae, 79(1/2):9– 16.[McCrorie and Chambers, 2006] McCrorie, J. R. and Chambers, M. J. (2006). Granger cau- sality and the sampling of economic processes. Journal of Econometrics, 132(2):311–336.[Medina, 2010] Medina, L. (2010). A Commodity Curse? The Dynamic Effects of Commo- dity Prices on Fiscal Performance in Latin America. MPRA Paper, 21690.[Phillips, 1973] Phillips, P. (1973). The problem of identification in finite parameter conti- nuous time models. Journal of Econometrics, 1(4):351–362.[Ryan et al., 2018] Ryan, O., Kuiper, R. M., and Hamaker, E. L. (2018). A Continuous-Time Approach to Intensive Longitudinal Data: What, Why, and How? In van Montfort, K., Oud, J. H. L., and Voelkle, M. C., editors, Continuous Time Modeling in the Behavioral and Related Sciences, pages 27–54. Springer International Publishing, Cham.[Van Loan, 1978] Van Loan, C. (1978). Computing integrals involving the matrix exponential. IEEE Transactions on Automatic Control, 23(3):395–404.[Voelkle and Oud, 2013] Voelkle, M. C. and Oud, J. H. L. (2013). Continuous time modelling with individually varying time intervals for oscillating and non-oscillating processes: Continuous time modelling. British Journal of Mathematical and Statistical Psychology, 66(1):103–126.[Voelkle et al., 2012] Voelkle, M. C., Oud, J. H. L., Davidov, E., and Schmidt, P. (2012). An SEM approach to continuous time modeling of panel data: Relating authoritarianism and anomia: Correction to Voelkle, Oud, Davidov, and Schmidt (2012). Psychological Methods, 17(3):384–384.C1 Métodos y Metodología Econométrica y Estadística: GeneralC32 - Modelos de series temporalesC33 - Modelos con datos de panelSeries de tiempoTiempo ContinuoEconometríaVARPVARORIGINAL1020804008.2023.pdf1020804008.2023.pdfTesis de Maestría en Ciencias - Estadísticaapplication/pdf347783https://repositorio.unal.edu.co/bitstream/unal/83988/4/1020804008.2023.pdf40ad3e4174504e8eabaf3e4f683201e9MD54LICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/83988/5/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD55THUMBNAIL1020804008.2023.pdf.jpg1020804008.2023.pdf.jpgGenerated Thumbnailimage/jpeg4279https://repositorio.unal.edu.co/bitstream/unal/83988/6/1020804008.2023.pdf.jpg4b86a43df2ff54b32268a5c1a7a53d69MD56unal/83988oai:repositorio.unal.edu.co:unal/839882023-08-08 23:03:43.668Repositorio Institucional Universidad Nacional de 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