A comparison of micro versus macro point estimators for markov-process models

Abstract

We compare, by means of factorially designed Monte Carlo simulation experiments, the performance of (macro-data) restricted least-squares point estimators with that of (micro-data) maximum likelihood estimators for Markov-process models. We find, by various measures of estimator accuracy, that micro data are approximately ten times more valuable than macro data. We also find evidence that a small state space, a long time series, and a large number of entities observed enhance performance.

Keywords:

• Markov processes
• estimation of markov processes
• micro vs. macro data

^*This work was partially supported by a grant from the McKnight Foundation to the Carlson School of Management at the University of Minnesota. Computational support from the Minnesota Supercomputer Institute is also gratefully acknowledged.

^*This work was partially supported by a grant from the McKnight Foundation to the Carlson School of Management at the University of Minnesota. Computational support from the Minnesota Supercomputer Institute is also gratefully acknowledged.

Notes

^*This work was partially supported by a grant from the McKnight Foundation to the Carlson School of Management at the University of Minnesota. Computational support from the Minnesota Supercomputer Institute is also gratefully acknowledged.