Dynamic Factor Analysis for Short Panels: Estimating Performance Trajectories for Water Utilities
No hay miniatura disponible
Fecha
Título de la revista
ISSN de la revista
Título del volumen
Editor
Resumen
Descripción
We develop a dynamic factor model for panel data with a short time dimension (i.e.
n<15). Unlike most of the work in the DFM literature where one common factor is estimated
for a group of cross sectional units, our interest lies in the estimation of a latent
variable for each cross sectional unit at every point in time. This difference increases the
computational challenges of the estimation process. To facilitate estimation we develop
the “Two-Cycle Conditional Expectation-Maximization” (2CCEM) algorithm which is
a variant of the EM algorithm and it’s extensions (Dempster et al. 1977; Meng and
Rubin 1993; Liu and Rubin 1994). Initially, the latent variable is estimated (first cycle) and then the dynamic component is incorporated into the estimation process (second
cycle). The estimates of each cycle are updated with information from the estimates of
the previous cycle until convergence is achieved. We provide simulation results demonstrating
consistency of our 2CCEM estimator. One of the advantages of this work is that
the estimation strategy can account for multiple cross sectional units with a short time
dimension, and is flexible enough to be used in different types of applications. We apply
our model to a dataset of 853 water and sanitation utilities from 45 countries and use the
2CCEM algorithm to estimate performance trajectories for each utility.