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...

This study uses the Flexible Least Squares method for Time-Varying Linear Regression (FLS-TVLR) to investigate coefficient stability for the Goldfeld U.S. money demand model over the volatile period 1959:Q2 to 1985:Q3. The only constraint imposed on coefficient variation over time is a...

This study proposes a Flexible Least Squares (FLS) method for state estimation when the dynamic equations are unknown but the process state evolves only slowly over time. A smoothness prior is introduced in place of an explicit specification for the unknown dynamic equations governing the...

A "flexible least cost method" is proposed for investigating the basic compatibility of theory and observations in the absence of valid or known stochastic characterizations for residual error terms. Annotated pointers to related work can be accessed here:...

The logical relationship between the multicriteria Flexible Least Squares (FLS) method and Kalman filtering is clarified. Annotated pointers to related work can be accessed here: http://www.econ.iastate.edu/tesfatsi/flshome.htm

Discrepancies between assumed dynamical models and observations are often handled by making further probabilistic assumptions, a tactic which has both strengths and weaknesses. A re-examination of filtering and smoothing is conducted, and an alternative multicriteria Flexible Least Squares (FLS)...

This study develops a least-squares measure for simultaneously testing the basic compatibility of prior dynamical, observational, and distributional model specifications against actual data for a class of dynamic nonlinear economic models with parameters explicitly modeled as nonlinear functions...

This article develops a multicriteria "flexible least squares (FLS)" method for time-varying linear regression. The basic FLS objective is to determine the "residual efficiency frontier," that is, the set of all coefficient trajectory estimates that yield vector-minimal sums of squared residual...

This study develops two algorithms for the exact sequential updating of the optimal solution for a general discrete-time nonlinear least squares estimation problem as the process length increases and new observations are obtained. One algorithm proceeds via an imbedding on the process length and...