IDEAS home Printed from https://ideas.repec.org/p/vua/wpaper/1997-54.html
   My bibliography  Save this paper

Dynamic analysis of multivariate panel data with nonlinear transformations

Author

Listed:
  • Montfort, Kees van

    (Vrije Universiteit Amsterdam, Faculteit der Economische Wetenschappen en Econometrie (Free University Amsterdam, Faculty of Economics Sciences, Business Administration and Economitrics)

  • Bijleveld, Catrien

Abstract

Many models for multivariate data analysis can be seen as special cases of the linear dynamic or state space model. Contrary to the classical approach to linear dynamic systems analysis, the model presented here is developed from the social science framework of approximation, data reduction and interpretation, where generalization is required not only over time points but over subjects as well. Borrowing concepts from the theory on mixture distributions, the linear dynamic model can be viewed as a multilayered regression model, in which the output variables are imprecise manifestations of an unobserved continuous process. An additional layer of mixing makes it possible to incorporate non-normal as well as ordinal variables. Using the EM-algorithm, we find estimates of the unknown mode parameters, simultaneously providing stability estimates. We illustrate the applicability of the obtained procedure through an empirical example.

Suggested Citation

  • 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.
    • Handle: RePEc:vua:wpaper:1997-54
    as

    Download full text from publisher

    File URL: http://degree.ubvu.vu.nl/repec/vua/wpaper/pdf/19970054.pdf
    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. Michael Browne, 1992. "Circumplex models for correlation matrices," Psychometrika, Springer;The Psychometric Society, vol. 57(4), pages 469-497, December.
    3. David Rogosa & John Willett, 1985. "Understanding correlates of change by modeling individual differences in growth," Psychometrika, Springer;The Psychometric Society, vol. 50(2), pages 203-228, June.
    4. Rolf Langeheine & Frank Van De Pol, 1990. "A Unifying Framework for Markov Modeling in Discrete Space and Discrete Time," Sociological Methods & Research, , vol. 18(4), pages 416-441, May.
    5. Frank Van De Pol & Jan De Leeuw, 1986. "A Latent Markov Model to Correct for Measurement Error," Sociological Methods & Research, , vol. 15(1-2), pages 118-141, November.
    6. 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.
    7. Catrien Bijleveld & Jan Leeuw, 1991. "Fitting longitudinal reduced-rank regression models by alternating least squares," Psychometrika, Springer;The Psychometric Society, vol. 56(3), pages 433-447, September.
    8. Stef Buuren, 1997. "Fitting arma time series by structural equation models," Psychometrika, Springer;The Psychometric Society, vol. 62(2), pages 215-236, June.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    3. 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.
    4. 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.
    5. Sun-Joo Cho & Sarah Brown-Schmidt & Woo-yeol Lee, 2018. "Autoregressive Generalized Linear Mixed Effect Models with Crossed Random Effects: An Application to Intensive Binary Time Series Eye-Tracking Data," Psychometrika, Springer;The Psychometric Society, vol. 83(3), pages 751-771, September.
    6. 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.
    7. 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.
    8. Guangjian Zhang & Sy-Miin Chow & Anthony Ong, 2011. "A Sandwich-Type Standard Error Estimator of SEM Models with Multivariate Time Series," Psychometrika, Springer;The Psychometric Society, vol. 76(1), pages 77-96, January.
    9. 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).
    10. Acedo, Francisco J. & Coviello, Nicole & Agustí, María, 2021. "Caution ahead! The long-term effects of initial export intensity and geographic dispersion on INV development," Journal of World Business, Elsevier, vol. 56(6).
    11. Kennon M. Sheldon & Evgeny N. Osin & Tamara O. Gordeeva & Dmitry D. Suchkov & Vlaidslav V. Bobrov & Elena I. Rasskazova & Oleg A. Sychev, 2015. "Evaluating the Dimensionality of the Relative Autonomy Continuum in Us and Russian Samples," HSE Working papers WP BRP 48/PSY/2015, National Research University Higher School of Economics.
    12. Krijnen, Wim P., 2006. "Convergence of the sequence of parameters generated by alternating least squares algorithms," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 481-489, November.
    13. Eichler, Michael & Motta, Giovanni & von Sachs, Rainer, 2011. "Fitting dynamic factor models to non-stationary time series," Journal of Econometrics, Elsevier, vol. 163(1), pages 51-70, July.
    14. Stijn Schelfhout & Bart Wille & Lot Fonteyne & Elisabeth Roels & Filip De Fruyt & Wouter Duyck, 2019. "The effects of vocational interest on study results: Student person – environment fit and program interest diversity," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-26, April.
    15. Kaestner, Robert & Grossman, Michael, 2009. "Effects of weight on children's educational achievement," Economics of Education Review, Elsevier, vol. 28(6), pages 651-661, December.
    16. Peter Lenk & Michel Wedel & Ulf Böckenholt, 2006. "Bayesian Estimation of Circumplex Models Subject to Prior Theory Constraints and Scale-Usage Bias," Psychometrika, Springer;The Psychometric Society, vol. 71(1), pages 33-55, March.
    17. Tenko Raykov, 2001. "On the Use and Utility of the Reliability Coefficient in Social and Behavioral Research," Quality & Quantity: International Journal of Methodology, Springer, vol. 35(3), pages 253-263, August.
    18. Breen, Richard & Moisio, Pasi, 2003. "Poverty dynamics corrected for measurement error," ISER Working Paper Series 2003-17, Institute for Social and Economic Research.
    19. Bouaddi, S. & Ihlal, A. & Fernández-García, A., 2017. "Comparative analysis of soiling of CSP mirror materials in arid zones," Renewable Energy, Elsevier, vol. 101(C), pages 437-449.
    20. 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.

    More about this item

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:vua:wpaper:1997-54. 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: R. Dam (email available below). General contact details of provider: https://edirc.repec.org/data/fewvunl.html .

    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.