Dynamic factor analysis for short panels: estimating performance trajectories for water utilities
Nikolaos Zirogiannis () and
Yorghos Tripodis ()
Additional contact information
Nikolaos Zirogiannis: Indiana University Bloomington
Yorghos Tripodis: Boston University
Statistical Methods & Applications, 2018, vol. 27, issue 1, 131-150
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.
Keywords: Dynamic factor models; EM algorithm; Panel data; State-space models; Water utilities (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
http://link.springer.com/10.1007/s10260-017-0394-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:spr:stmapp:v:27:y:2018:i:1:d:10.1007_s10260-017-0394-y
Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10260/PS2
Access Statistics for this article
Statistical Methods & Applications is currently edited by Tommaso Proietti
More articles in Statistical Methods & Applications from Springer, Società Italiana di Statistica
Bibliographic data for series maintained by Sonal Shukla ().