IDEAS home Printed from https://ideas.repec.org/a/spr/psycho/v65y2000i2p199-215.html
   My bibliography  Save this article

Continuous time state space modeling of panel data by means of sem

Author

Listed:
  • Johan Oud
  • Robert Jansen

Abstract

No abstract is available for this item.

Suggested Citation

  • Johan Oud & Robert Jansen, 2000. "Continuous time state space modeling of panel data by means of sem," Psychometrika, Springer;The Psychometric Society, vol. 65(2), pages 199-215, June.
    • Handle: RePEc:spr:psycho:v:65:y:2000:i:2:p:199-215
    DOI: 10.1007/BF02294374
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/BF02294374
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/BF02294374?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bergstrom, A.R., 1984. "Continuous time stochastic models and issues of aggregation over time," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 20, pages 1145-1212, Elsevier.
    2. Hansen, Lars Peter & Sargent, Thomas J, 1983. "The Dimensionality of the Aliasing Problem in Models with Rational Spectral Densities," Econometrica, Econometric Society, vol. 51(2), pages 377-387, March.
    3. repec:cup:etheor:v:9:y:1993:i:2:p:283-95 is not listed on IDEAS
    4. Singer, Hermann, 1995. "Analytical Score Function for Irregularly Sampled Continuous Time Stochastic Processes with Control Variables and Missing Values," Econometric Theory, Cambridge University Press, vol. 11(4), pages 721-735, August.
    5. Hamerle, Alfred & Singer, Hermann & Nagl, Willi, 1993. "Identification and Estimation of Continuous Time Dynamic Systems With Exogenous Variables Using Panel Data," Econometric Theory, Cambridge University Press, vol. 9(2), pages 283-295, April.
    6. R. A. R. G. Jansen & J. H. L. Oud, 1995. "Longitudinal LISREL model estimation from incomplete panel data using the EM algorithm and the Kalman smoother," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 49(3), pages 362-377, November.
    7. Phillips, P. C. B., 1973. "The problem of identification in finite parameter continuous time models," Journal of Econometrics, Elsevier, vol. 1(4), pages 351-362, December.
    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. Johan Oud & Manuel Voelkle, 2014. "Do missing values exist? Incomplete data handling in cross-national longitudinal studies by means of continuous time modeling," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(6), pages 3271-3288, November.
    2. Sy‐Miin Chow & Guangjian Zhang, 2008. "Continuous‐time modelling of irregularly spaced panel data using a cubic spline model," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 62(1), pages 131-154, February.
    3. Michael D. Hunter & Haya Fatimah & Marina A. Bornovalova, 2022. "Two Filtering Methods of Forecasting Linear and Nonlinear Dynamics of Intensive Longitudinal Data," Psychometrika, Springer;The Psychometric Society, vol. 87(2), pages 477-505, June.
    4. J. Oud, 2010. "Second-order stochastic differential equation model as an alternative for the ALT and CALT models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 94(2), pages 203-215, June.
    5. John McArdle, 2011. "Longitudinal dynamic analyses of cognition in the health and retirement study panel," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(4), pages 453-480, December.
    6. Øystein Sørensen & Anders M. Fjell & Kristine B. Walhovd, 2023. "Longitudinal Modeling of Age-Dependent Latent Traits with Generalized Additive Latent and Mixed Models," Psychometrika, Springer;The Psychometric Society, vol. 88(2), pages 456-486, June.
    7. Lijuan Wang & Samantha F. Anderson, 2016. "A Review of Intensive Longitudinal Methods: An Introduction to Diary and Experience Sampling Research," Journal of Educational and Behavioral Statistics, , vol. 41(6), pages 653-658, December.
    8. Yanling Li & Zita Oravecz & Shuai Zhou & Yosef Bodovski & Ian J. Barnett & Guangqing Chi & Yuan Zhou & Naomi P. Friedman & Scott I. Vrieze & Sy-Miin Chow, 2022. "Bayesian Forecasting with a Regime-Switching Zero-Inflated Multilevel Poisson Regression Model: An Application to Adolescent Alcohol Use with Spatial Covariates," Psychometrika, Springer;The Psychometric Society, vol. 87(2), pages 376-402, June.
    9. Siem Jan Koopman & Marius Ooms & André Lucas & Kees van Montfort & Victor Van Der Geest, 2008. "Estimating systematic continuous‐time trends in recidivism using a non‐Gaussian panel data model," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 62(1), pages 104-130, February.
    10. Sy-Miin Chow & Zhaohua Lu & Andrew Sherwood & Hongtu Zhu, 2016. "Fitting Nonlinear Ordinary Differential Equation Models with Random Effects and Unknown Initial Conditions Using the Stochastic Approximation Expectation–Maximization (SAEM) Algorithm," Psychometrika, Springer;The Psychometric Society, vol. 81(1), pages 102-134, March.
    11. Sy-Miin Chow & Lu Ou & Arridhana Ciptadi & Emily B. Prince & Dongjun You & Michael D. Hunter & James M. Rehg & Agata Rozga & Daniel S. Messinger, 2018. "Representing Sudden Shifts in Intensive Dyadic Interaction Data Using Differential Equation Models with Regime Switching," Psychometrika, Springer;The Psychometric Society, vol. 83(2), pages 476-510, June.
    12. Zhao-Hua Lu & Sy-Miin Chow & Nilam Ram & Pamela M. Cole, 2019. "Zero-Inflated Regime-Switching Stochastic Differential Equation Models for Highly Unbalanced Multivariate, Multi-Subject Time-Series Data," Psychometrika, Springer;The Psychometric Society, vol. 84(2), pages 611-645, June.
    13. Marc J. M. H. Delsing & Johan H. L. Oud, 2008. "Analyzing reciprocal relationships by means of the continuous‐time autoregressive latent trajectory model," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 62(1), pages 58-82, February.
    14. Oisín Ryan & Ellen L. Hamaker, 2022. "Time to Intervene: A Continuous-Time Approach to Network Analysis and Centrality," Psychometrika, Springer;The Psychometric Society, vol. 87(1), pages 214-252, March.
    15. Julie Wood & Zita Oravecz & Nina Vogel & Lizbeth Benson & Sy-Miin Chow & Pamela Cole & David E Conroy & Aaron L Pincus & Nilam Ram, 2018. "Modeling Intraindividual Dynamics Using Stochastic Differential Equations: Age Differences in Affect Regulation," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 73(1), pages 171-184.
    16. Hermann Singer, 2011. "Continuous-discrete state-space modeling of panel data with nonlinear filter algorithms," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(4), pages 375-413, December.
    17. Chen, Yunxiao & Zhang, Siliang, 2020. "A latent Gaussian process model for analysing intensive longitudinal data," LSE Research Online Documents on Economics 101121, London School of Economics and Political Science, LSE Library.
    18. Yusep Suparman & Henk Folmer & Johan H.L. Oud, 2016. "The willingness to pay for in-house piped water in urban and rural Indonesia," Papers in Regional Science, Wiley Blackwell, vol. 95(2), pages 407-426, June.

    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. Chambers, MJ & McCrorie, JR & Thornton, MA, 2017. "Continuous Time Modelling Based on an Exact Discrete Time Representation," Economics Discussion Papers 20497, University of Essex, Department of Economics.
    2. Yu, Jun, 2014. "Econometric Analysis Of Continuous Time Models: A Survey Of Peter Phillips’S Work And Some New Results," Econometric Theory, Cambridge University Press, vol. 30(4), pages 737-774, August.
    3. Wang, Xiaohu & Phillips, Peter C.B. & Yu, Jun, 2011. "Bias in estimating multivariate and univariate diffusions," Journal of Econometrics, Elsevier, vol. 161(2), pages 228-245, April.
    4. Magnus, Jan R. & Pijls, Henk G.J. & Sentana, Enrique, 2021. "The Jacobian of the exponential function," Journal of Economic Dynamics and Control, Elsevier, vol. 127(C).
    5. Hermann Singer, 2011. "Continuous-discrete state-space modeling of panel data with nonlinear filter algorithms," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(4), pages 375-413, December.
    6. Peter C.B.Phillips & Jun Yu, "undated". "Maximum Likelihood and Gaussian Estimation of Continuous Time Models in Finance," Working Papers CoFie-08-2009, Singapore Management University, Sim Kee Boon Institute for Financial Economics.
    7. Jun Yu, 2009. "Econometric Analysis of Continuous Time Models : A Survey of Peter Phillips’ Work and Some New Results," Microeconomics Working Papers 23046, East Asian Bureau of Economic Research.
    8. Zadrozny, Peter A., 2016. "Extended Yule–Walker identification of VARMA models with single- or mixed-frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 438-446.
    9. Hansen, Lars Peter & Scheinkman, Jose Alexandre, 1995. "Back to the Future: Generating Moment Implications for Continuous-Time Markov Processes," Econometrica, Econometric Society, vol. 63(4), pages 767-804, July.
    10. Jeremy Berkowitz, 2000. "On identification of continuous time stochastic processes," Finance and Economics Discussion Series 2000-07, Board of Governors of the Federal Reserve System (U.S.).
    11. Seungmoon Choi, 2011. "Closed-Form Likelihood Expansions for Multivariate Time-Inhomogeneous Diffusions," School of Economics and Public Policy Working Papers 2011-26, University of Adelaide, School of Economics and Public Policy.
    12. Choi, Seungmoon, 2015. "Explicit form of approximate transition probability density functions of diffusion processes," Journal of Econometrics, Elsevier, vol. 187(1), pages 57-73.
    13. Chambers, M.J. & McCrorie, J.R., 2004. "Frequency Domain Gaussian Estimation of Temporally Aggregated Cointegrated Systems," Other publications TiSEM 0d3ed468-36ef-4baf-8339-8, Tilburg University, School of Economics and Management.
    14. Henghsiu Tsai & K. S. Chan, 2005. "Quasi‐Maximum Likelihood Estimation for a Class of Continuous‐time Long‐memory Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(5), pages 691-713, September.
    15. Vicky Fasen-Hartmann & Celeste Mayer, 2022. "Whittle estimation for continuous-time stationary state space models with finite second moments," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(2), pages 233-270, April.
    16. Choi, Seungmoon, 2013. "Closed-form likelihood expansions for multivariate time-inhomogeneous diffusions," Journal of Econometrics, Elsevier, vol. 174(2), pages 45-65.
    17. Chaohua Dong & Jiti Gao, 2012. "Expansion of Lévy Process Functionals and Its Application in Statistical Estimation," Monash Econometrics and Business Statistics Working Papers 2/12, Monash University, Department of Econometrics and Business Statistics.
    18. Lars Peter Hansen & Thomas J. Sargent, 1982. "Formulating and estimating continuous time rational expectations models," Staff Report 75, Federal Reserve Bank of Minneapolis.
    19. Chaohua Dong & Jiti Gao, 2011. "Expansion of Brownian Motion Functionals and Its Application in Econometric Estimation," Monash Econometrics and Business Statistics Working Papers 19/11, Monash University, Department of Econometrics and Business Statistics.
    20. Chambers, Marcus J. & Roderick McCrorie, J., 2007. "Frequency domain estimation of temporally aggregated Gaussian cointegrated systems," Journal of Econometrics, Elsevier, vol. 136(1), pages 1-29, January.

    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:spr:psycho:v:65:y:2000:i:2:p:199-215. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.