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A Generalized Dynamic Factor Model for Panel Data: Estimation with a Two-Cycle Conditional Expectation-Maximization Algorithm

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  • Nikolaos Zirogiannis

    () (Department of Resource Economics, University of Massachusetts Amherst)

  • Yorghos Tripodis

    () (Department of Biostatistics, Boston University School of Public Health)

Abstract

We develop a generalized dynamic factor model for panel data with the goal of estimating an unobserved index. While similar models have been developed in the literature of dynamic factor analysis, our contribution is threefold. First, contrary to simple dynamic factor analysis where multiple attributes of the same subject are measured at each time period, our model also accounts for multiple subjects. It is therefore suitable to a panel data framework. Second, our model estimates a unique unobserved index for every subject for every time period, as opposed to previous work where a temporal index common to all subjects was used. Third, we develop a novel iterative estimation process which we call the Two-Cycle Conditional Expectation-Maximization (2CCEM) algorithm and is flexible enough to handle a variety of different types of datasets. The model is applied on a panel measuring attributes related to the operation of water and sanitation utilities.

Suggested Citation

  • Nikolaos Zirogiannis & Yorghos Tripodis, 2013. "A Generalized Dynamic Factor Model for Panel Data: Estimation with a Two-Cycle Conditional Expectation-Maximization Algorithm," Working Papers 2013-1, University of Massachusetts Amherst, Department of Resource Economics.
  • Handle: RePEc:dre:wpaper:2013-1
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    File URL: http://courses.umass.edu/resec/workingpapers/documents/ResEcWorkingPaper2013-1.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    Dynamic Factor Models; EM algorithm; Panel Data; State-Space models; IBNET;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • Q25 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Water

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