IDEAS home Printed from https://ideas.repec.org/p/cte/wsrepe/ws012415.html
   My bibliography  Save this paper

Multivariate analysis in vector time series

Author

Listed:
  • Galeano, Pedro

Abstract

This paper reviews the applications of classical multivariate techniques for discrimination, clustering and dimension reduction for time series data. It is shown that the discrimination problem can be seen as a model selection problem. Some of the results obtained in the time domain are reviewed. Clustering time series requires the definition of an adequate metric between univariate time series and several possible metrics are analyzed. Dimension reduction has been a very active line of research in the time series literature and the dynamic principal components or canonical analysis of Box and Tiao (1977) and the factor model as developed by Peña and Box (1987) and Peña and Poncela (1998) are analyzed. The relation between the nonstationary factor model and the cointegration literature is also reviewed.

Suggested Citation

  • Galeano, Pedro, 2001. "Multivariate analysis in vector time series," DES - Working Papers. Statistics and Econometrics. WS ws012415, Universidad Carlos III de Madrid. Departamento de Estadística.
    • Handle: RePEc:cte:wsrepe:ws012415
    as

    Download full text from publisher

    File URL: https://e-archivo.uc3m.es/rest/api/core/bitstreams/67d98e3b-0aff-4302-81b8-bf8fe5a690ed/content
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Peter Molenaar & Jan Gooijer & Bernhard Schmitz, 1992. "Dynamic factor analysis of nonstationary multivariate time series," Psychometrika, Springer;The Psychometric Society, vol. 57(3), pages 333-349, September.
    2. Peter Molenaar, 1985. "A dynamic factor model for the analysis of multivariate time series," Psychometrika, Springer;The Psychometric Society, vol. 50(2), pages 181-202, June.
    3. repec:cte:wsrepe:3680 is not listed on IDEAS
    4. Javier Alagón, 1989. "Spectral Discrimination For Two Groups Of Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 10(3), pages 203-214, May.
    5. Chaudhuri, G., 1992. "Linear discriminant function for complex normal time series," Statistics & Probability Letters, Elsevier, vol. 15(4), pages 277-279, November.
    6. G. R. Dargahi‐Noubary & P. J. Laycock, 1981. "Spectral Ratio Discriminants And Information Theory," Journal of Time Series Analysis, Wiley Blackwell, vol. 2(2), pages 71-86, March.
    7. A. T. Walden, 1994. "Spatial Clustering: Using Simple Summaries of Seismic Data to Find the Edge of an Oil‐Field," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 43(2), pages 385-398, June.
    8. Alvaro Escribano & Daniel Peña, 1994. "Cointegration And Common Factors," Journal of Time Series Analysis, Wiley Blackwell, vol. 15(6), pages 577-586, November.
    9. Domenico Piccolo, 1990. "A Distance Measure For Classifying Arima Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 11(2), pages 153-164, March.
    10. Geweke, John F & Singleton, Kenneth J, 1981. "Maximum Likelihood "Confirmatory" Factor Analysis of Economic Time Series," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 22(1), pages 37-54, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sipan Aslan & Ceylan Yozgatligil & Cem Iyigun, 2018. "Temporal clustering of time series via threshold autoregressive models: application to commodity prices," Annals of Operations Research, Springer, vol. 260(1), pages 51-77, January.
    2. Beibei Zhang & Rong Chen, 2018. "Nonlinear Time Series Clustering Based on Kolmogorov-Smirnov 2D Statistic," Journal of Classification, Springer;The Classification Society, vol. 35(3), pages 394-421, October.
    3. Alonso, A.M. & Berrendero, J.R. & Hernandez, A. & Justel, A., 2006. "Time series clustering based on forecast densities," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 762-776, November.
    4. Caiado, Jorge & Crato, Nuno & Pena, Daniel, 2006. "A periodogram-based metric for time series classification," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2668-2684, June.
    5. Sonia Díaz & José Vilar, 2010. "Comparing Several Parametric and Nonparametric Approaches to Time Series Clustering: A Simulation Study," Journal of Classification, Springer;The Classification Society, vol. 27(3), pages 333-362, November.
    6. Druica, Elena & Goschin, Zizi, 2016. "Does Economic Status Matter for the Regional Variation of Malnutrition-Related Diabetes in Romania? Temporal Clustering and Spatial Analyses," MPRA Paper 88831, University Library of Munich, Germany.
    7. Benny Ren & Ian Barnett, 2022. "Autoregressive mixture models for clustering time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(6), pages 918-937, November.
    8. Giovanni De Luca & Paola Zuccolotto, 2011. "A tail dependence-based dissimilarity measure for financial time series clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 5(4), pages 323-340, December.
    9. Irene Mariñas-Collado & Ana E. Sipols & M. Teresa Santos-Martín & Elisa Frutos-Bernal, 2022. "Clustering and Forecasting Urban Bus Passenger Demand with a Combination of Time Series Models," Mathematics, MDPI, vol. 10(15), pages 1-16, July.
    10. Ángel Cuevas & Enrique Quilis, 2012. "A factor analysis for the Spanish economy," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 3(3), pages 311-338, September.
    11. Giovanni De Luca & Paola Zuccolotto, 2017. "Dynamic tail dependence clustering of financial time series," Statistical Papers, Springer, vol. 58(3), pages 641-657, September.
    12. C. Cosculluela-Martínez & R. Ibar-Alonso & G. J. D. Hewings, 2019. "Life Expectancy Index: Age Structure of Population and Environment Evolution," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 142(2), pages 507-522, April.
    13. Montero, Pablo & Vilar, José A., 2014. "TSclust: An R Package for Time Series Clustering," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 62(i01).
    14. Mendes, Beatriz V.M. & Leal, Ricardo P.C. & Carvalhal-da-Silva, Andre, 2007. "Clustering in emerging equity markets," Emerging Markets Review, Elsevier, vol. 8(3), pages 194-205, September.
    15. Corduas, Marcella & Piccolo, Domenico, 2008. "Time series clustering and classification by the autoregressive metric," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1860-1872, January.
    16. Heung-gu Son & Yunsun Kim & Sahm Kim, 2020. "Time Series Clustering of Electricity Demand for Industrial Areas on Smart Grid," Energies, MDPI, vol. 13(9), pages 1-14, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Stef Buuren, 1997. "Fitting arma time series by structural equation models," Psychometrika, Springer;The Psychometric Society, vol. 62(2), pages 215-236, June.
    2. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    3. Peter Molenaar & John Nesselroade, 2001. "Rotation in the dynamic factor modeling of multivariate stationary time series," Psychometrika, Springer;The Psychometric Society, vol. 66(1), pages 99-107, March.
    4. Sy-Miin Chow & Guangjian Zhang, 2013. "Nonlinear Regime-Switching State-Space (RSSS) Models," Psychometrika, Springer;The Psychometric Society, vol. 78(4), pages 740-768, October.
    5. Peter Molenaar, 1999. "Comment on fitting MA time series by structural equation models," Psychometrika, Springer;The Psychometric Society, vol. 64(1), pages 91-94, March.
    6. Ortega, Jose Antonio & Poncela, Pilar, 2005. "Joint forecasts of Southern European fertility rates with non-stationary dynamic factor models," International Journal of Forecasting, Elsevier, vol. 21(3), pages 539-550.
    7. Fei Gu & Kristopher J. Preacher & Emilio Ferrer, 2014. "A State Space Modeling Approach to Mediation Analysis," Journal of Educational and Behavioral Statistics, , vol. 39(2), pages 117-143, April.
    8. Pena, Daniel & Poncela, Pilar, 2004. "Forecasting with nonstationary dynamic factor models," Journal of Econometrics, Elsevier, vol. 119(2), pages 291-321, April.
    9. Emma SARNO & Alberto ZAZZARO, 2003. "Structural Convergence of Macroeconomic Time Series: Evidence for Inflation Rates in EU Countries," Working Papers 180, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    10. Niansheng Tang & Sy-Miin Chow & Joseph G. Ibrahim & Hongtu Zhu, 2017. "Bayesian Sensitivity Analysis of a Nonlinear Dynamic Factor Analysis Model with Nonparametric Prior and Possible Nonignorable Missingness," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 875-903, December.
    11. Corduas, Marcella & Piccolo, Domenico, 2008. "Time series clustering and classification by the autoregressive metric," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1860-1872, January.
    12. Nikolaos Zirogiannis & Yorghos Tripodis, 2018. "Dynamic factor analysis for short panels: estimating performance trajectories for water utilities," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(1), pages 131-150, March.
    13. Carfora, Alfonso & Scandurra, Giuseppe & Thomas, Antonio, 2022. "Forecasting the COVID-19 effects on energy poverty across EU member states," Energy Policy, Elsevier, vol. 161(C).
    14. Montfort, Kees van & Bijleveld, Catrien, 1997. "Dynamic analysis of multivariate panel data with nonlinear transformations," Serie Research Memoranda 0054, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    15. Allaudeen Hameed, 1997. "Time-Varying Factors And Cross-Autocorrelations In Short-Horizon Stock Returns," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 20(4), pages 435-458, December.
    16. Fabio H. Nieto & Luis Fernando Melo, 2001. "About a Coincident Index for the State of the Economy," Borradores de Economia 1938, Banco de la Republica.
    17. Huh, Hyeon-seung & Kim, David, 2013. "An empirical test of exogenous versus endogenous growth models for the G-7 countries," Economic Modelling, Elsevier, vol. 32(C), pages 262-272.
    18. Katalin Varga & Tibor Szendrei, 2024. "Non-stationary Financial Risk Factors and Macroeconomic Vulnerability for the UK," Papers 2404.01451, arXiv.org.
    19. Qingliang Zhao & Junji Chen & Xiaobin Feng & Yiduo Wang, 2024. "A Novel Bézier LSTM Model: A Case Study in Corn Analysis," Mathematics, MDPI, vol. 12(15), pages 1-21, July.
    20. Dickhaus, Thorsten & Sirotko-Sibirskaya, Natalia, 2019. "Simultaneous statistical inference in dynamic factor models: Chi-square approximation and model-based bootstrap," Computational Statistics & Data Analysis, Elsevier, vol. 129(C), pages 30-46.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cte:wsrepe:ws012415. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ana Poveda (email available below). General contact details of provider: http://portal.uc3m.es/portal/page/portal/dpto_estadistica .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.