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Dynamic factor analysis for short panels: estimating performance trajectories for water utilities

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  • Nikolaos Zirogiannis

    (Indiana University Bloomington)

  • Yorghos Tripodis

    (Boston University)

Abstract

We develop a novel estimation algorithm for a dynamic factor model (DFM) applied to panel data with a short time dimension and a large cross sectional dimension. Current DFMs usually require panels with a minimum of 20 years of quarterly data (80 time observations per panel). In contrast, the application we consider includes panels with a median of 8 annual observations. As a result, the time dimension in our paper is substantially shorter than previous work in the DFM literature. This difference increases the computational challenges of the estimation process which we address by developing the “Two-Cycle Conditional Expectation - Maximization” (2CCEM) algorithm which is a variant of the EM algorithm and its extensions. We analyze the conditions under which our model is identified and provide simulation results demonstrating consistency of our 2CCEM estimator. We apply the DFM to a dataset of 802 water and sanitation utilities from 43 countries and use the 2CCEM algorithm in order to estimate dynamic performance trajectories for each utility.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:stmapp:v:27:y:2018:i:1:d:10.1007_s10260-017-0394-y
    DOI: 10.1007/s10260-017-0394-y
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    Cited by:

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    2. Nikolaos Zirogiannis & Kerry Krutilla & Yorghos Tripodis & Kathryn Fledderman, 2019. "Human Development Over Time: An Empirical Comparison of a Dynamic Index and the Standard HDI," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 142(2), pages 773-798, April.

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