An Organizing Principle for Dynamic Estimation

This study develops a general multicriteria Flexible Least Squares (FLS) framework for the sequential estimation of process states. Three well-known state estimation algorithms (the Viterbi, Larson-Peschon, and Kalman filters) are derived as monocriterion specializations. The FLS framework is used to clarify both Bayesian and classical statistical procedures for treating potential model specification errors. Recently developed bicriteria specializations (FLS-TVLR, GFLS-ALS), explicitly designed to take model specification errors into account, are also reviewed. The latter specializations concretely demonstrate how the FLS framework can be used to construct estimation algorithms capable of handling disparate sources of information coherently and systematically, without forced scalarization. Annotated pointers to related work can be accessed here: http://www.econ.iastate.edu/tesfatsi/flshome.htm