A Heuristic Partial-Least-Squares Approach to Estimating Dynamic Path Models

  • Hans Gerhard Strohe
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


An approach to dynamic modelling with latent variables is proposed. It has been developed on the base of H. Wold’s Partial Least Squares (PLS). An operator matrix containing the lag operator L is substituted for the path coefficient matrix of Wold’s static PLS model. On what is called the dynamic PLS model (DPLS) the original PLS estimation algorithm is virtually applicable. Lagged and leaded latent variables are used in the iterative process of estimating the weights of the manifest variables. The path coefficients are estimated by OLS or an appropriate dynamic modelling method The redundancy coefficient allows to measure the forecasting validity. DPLS has been programmed in PC-ISP/DGS©. Some properties of DPLS will be shown by simulation.


Partial Little Square Path Coefficient Path Model Partial Little Square Model Outer Model 
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Copyright information

© Springer-Verlag Berlin · Heidelberg 1998

Authors and Affiliations

  • Hans Gerhard Strohe
    • 1
  1. 1.Lehrstuhl für StatistikUniversität PotsdamGermany

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