A FORTRAN program for time-varying linear regression via flexible least squares
type: Item
instance of: scholarly article
title: A FORTRAN program for time-varying linear regression via flexible least squares
zbMATH Open document ID: 0726.62153
DOI: 10.1016/0167-9473(89)90029-7
author: Nima Rasakhoo
author: Leigh Tesfatsion
author: R. E. Kalaba
published in: Computational Statistics and Data Analysis
publication date: 1989-01-01T00:00:00Z
review text: Suppose the prior theoretical beliefs concerning the generation of a time-series data set take two forms: a prior measurement specification that the data has been generated by a linear regression model; and a prior dynamic specification that the regression coefficients evolve only slowly over time, if at all. The objective is to understand the actual relationship between the observed data and the regressor variables. In particular, do the estimated regression coefficients display any systematic time-variation? Is time-constancy a reasonably satisfactory approximation? The ``flexible least squares'' solution is defined to be the collection of all coefficient sequences estimates which yield vector- minimal sums of squared residual measurement and dynamic modelling errors for the given observations - i.e. which attain the ``residual efficiency frontier''. A user-friendly FORTRAN program (FLS) is now available for generating the frontier estimates.
Mathematics Subject Classification ID: 62M10
Mathematics Subject Classification ID: 62-04
Mathematics Subject Classification ID: 62J05
zbMATH DE Number: 4199395
zbMATH Keywords: time-varying coefficients
zbMATH Keywords: smoothness prior
zbMATH Keywords: simulation experiments
zbMATH Keywords: time-series
zbMATH Keywords: flexible least squares
zbMATH Keywords: FORTRAN program
MaRDI profile type: Publication
full work available at URL: 0167-9473(89)90029-7
OpenAlex ID: W1987645599
cites work: Q4082864
cites work: Tests of Equality Between Sets of Coefficients in Two Linear Regressions
cites work: Estimation in the Presence of Stochastic Parameter Variation
cites work: Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation
cites work: Some Algorithms for Linear Spline and Piecewise Multiple Linear Regression
cites work: Two Methods for Examining the Stability of Regression Coefficients
cites work: Partition Regression
cites work: Q4120006
cites work: The flexible least squares approach to time-varying linear regression
cites work: Exact sequential filtering, smoothing and prediction for nonlinear systems
cites work: Sequential nonlinear estimation with nonaugmented priors
cites work: The Estimation of the Parameters of a Linear Regression System Obeying Two Separate Regimes
cites work: Tests of the Hypothesis that a Linear Regression System Obeys Two Separate Regimes
cites work: U.S. money demand instability. A flexible least squares approach
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description: scientific article; zbMATH DE number 4199395