Flexible Least Squares
( Multicriteria Estimation)

Last Updated: 28 July 2008

Site Maintained By:
Leigh Tesfatsion
Professor of Economics and Mathematics
Heady Hall 375
Iowa State University
Ames, IA 50011-1070
Tel: (515) 294-7318
FAX: (515) 294-0221
http://www.econ.iastate.edu/tesfatsi/
tesfatsi AT iastate.edu

Table of Contents:

Overview

FLS Research Summary:

The theoretical relationships postulated for economic processes typically fall into four conceptually distinct categories: cross-sectional (simultaneous); dynamic; measurement; and probabilistic. Any actual economic process will inevitably behave in a manner that is incompatible to some degree with each postulated theoretical relationship. "All models are wrong, but some are useful." (GEP Box, 1979)

Consequently, the estimation of a model (postulated theory) conditional on given data would seem, intrinsically, to be a multicriteria optimization problem. Associated with any set of estimated (fitted) theoretical relationships will be a set of discrepancy terms reflecting the extent to which the estimated relationships are incompatible with the data. An econometrician undertaking the estimation would presumably prefer each type of discrepancy to be small. However, beyond a particular point a further decrease in one type of discrepancy comes at the cost of an increase in another.

In a series of studies listed in the following publications section and summarized in Kalaba and Tesfatsion (CSDA,1996 pdf 1.6MB), Bob Kalaba and I develop a multicriteria Flexible Least Squares (FLS) approach to model estimation that encompasses a wide range of views regarding the appropriate interpretation and treatment of theory-data discrepancy terms.

At one end of the range, stressing minimal reliance on stochastic priors for discrepancy terms, we develop FLS for Time-Varying Linear Regression (FLS-TVLR). For any given data and any given linear model (theory) proposed to explain the data, each possible estimated model generates two conceptually-distinct types of discrepancy terms, dynamic and measurement. The dynamic discrepancy terms reflect time variation in successive coefficient vectors (relative to a null of constancy), and the measurement discrepancy terms reflect differences between actual observed outcomes and theoretically predicted outcomes based on the null of a linear regression model. The dynamic and measurement errors are separately aggregated into sums of residual squared errors RD and RM.

The basic FLS-TVLR objective is to determine the Residual Efficiency Frontier (REF), i.e., the family of all estimated models that are equally efficient with respect to achieving vector-minimal squared error sums (RD,RM) for dynamic and measurement errors. By construction, given any estimated model M along the REF with residual squared error sums (RD,RM), the residual squared error sums (RD',RM') corresponding to any other estimated model M' are at least as large as the error sums (RD,RM) with either RD' > RD or RM' > RM.

We also develop Generalized FLS for Approximately Linear Systems (GFLS-ALS), an extension of FLS-TVLR to models for which the dynamic relationships and measurement relationships are both postulated to be general linear systems of equations. The concept of the REF is correspondingly generalized to a planar "cost-efficient frontier" in which costs are separately assessed for dynamic and measurement discrepancies.

As detailed in the software/manual section, below, our software implementations for FLS-TVLR and GFLS-ALS have been incorporated into the statistical packages SHAZAM and GAUSS.

More generally, suppose a model (theory) has been conjectured as a possible explanation for given data, and suppose the modeler has specified a K-dimensional vector of "incompatibility cost functions" for measuring the degree of incompatibility between theory and data in accordance with K different goodness-of-fit criteria. We develop a constructive FLS procedure for the determination of the corresponding Cost-Efficient Frontier (CEF), i.e., the family of all estimated models that are equally efficient with respect to achieving vector-minimalization of these K incompatibility cost functions. In particular, we derive a recurrence relation for updating the CEF at time t+1 as a function of the CEF at time t together with a K-dimensional vector of incremental incompatibility costs associated with new data obtained between time t and time t+1.

We show that this general K-dimensional CEF construction encompasses a number of other estimation methods. For example, for the time-varying linear regression (TVLR) problem, the REF determined via FLS-TVLR is a special case of the CEF with K=2. Moreover, in the special limiting case in which joint probability assessments can be used to achieve a complete amalgamation of all TVLR theory-data discrepancy terms into a single (K=1) real-valued incompatibility cost function taking the form of a posterior distribution, the CEF recurrence relation reduces to the standard Kalman Filter.

Finally, we also clarify the interesting relationship between FLS and the "general to specific" econometric methodology advocated by David Hendry and J.F. Richard, and between the FLS REF/CEF constructions and Ed Leamer's proposed information contract curve (global sensitivity analysis).

Budget-Line Analogy for FLS Frontiers (K > 1):

The question naturally arises whether an FLS residual efficiency frontier (REF) or cost-efficient frontier (CEF) of estimated models determined on the basis of K distinct goodness-of-fit criteria for K distinct types of discrepancy terms (K > 1) can be further reduced, ideally to a single "best" estimated model.

As will now be argued, this is akin to asking for the further reduction of budget lines (iso-cost curves) in standard economic analysis. Of course a single point can always be selected along a budget line by the introduction of a scalar-valued utility function inducing a specific preferential weighting across conceptually distinct types of goods and services. The key question, however, is whether this specific weighting can be done in a scientifically objective manner, assented to by others, or whether it simply represents the personal preferences of the researcher doing the weighting.

The argument can be made as follows:

NOTE: It is with immense sadness I report that Robert E. Kalaba, a great scholar, mentor, colleague, and friend, died on September 29, 2004.

Publications Developing the Basic FLS Methodology

FLS Software & Manual Availability

Illustrative FLS Applications