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Allows one to estimate and analyze dynamic, nonlinear, simultaneous equations models. The models can be rational expectations models, and they can have autoregressive errors of any order. The estimation techniques include OLS, 2SLS, 3SLS, FIML, LAD, 2SLAD, and some versions of Hansen's method of moments estimator.
Codes by Manfred Mudelsee. PearsonT estimates Pearson’s correlation coefficient from serially dependent time series. Rampfit estimates ramp function regressions. TAUEST estimates persistence in unevenly spaced weather/climate time series. XTREND estimates trends in the occurrence rate of extreme weather and climate events.
Solves the weighted orthogonal distance regression problem to find parameter estimates that minimize the sum of the squares of the weighted orthogonal distances between each observed data point and the curve described by a nonlinear equation. Includes documentation, revision history, and source code.
Developed by Robert E. Kalaba and Leigh Tesfatsion, implements the flexible least squares (FLS) approach to time-varying linear regression proposed by Kalaba and Tesfatsion in "Time-Varying Linear Regression Via Flexible Least Squares," Computers and Mathematics With Applications 17 (1989), 1215-1245. The FLS program has been incorporated into the statistical packages SHAZAM (Version 8.0) and GAUSS (TSM version 1.2).