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Kernel method to estimate nonlinear structural equation models

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Abstract

The purpose of this paper is to provide a nonparametric kernel method to estimate nonlinear structural equation models involving the functional effects between the latent variables. This approach is based on the combination of Principal Component Analysis (PCA) and kernel smoothing technique. The results obtained from different simulations on both nonlinear and linear structural models show the great performance of this method. Furthermore, an application on real data using a recovery satisfaction model is presented in this paper. From where, we show the adequacy of our method in capturing the non linearity between some latent variables.

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Authors and Affiliations

  1. Faculty of Sciences, Mohamed First University, Oujda, Morocco

    Ahmed Ouazza & Noureddine Rhomari

  2. Faculty of Legal, Economic and Social Sciences, Mohamed First University, Oujda, Morocco

    Zoubir Zarrouk

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  1. Ahmed Ouazza
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  2. Noureddine Rhomari
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  3. Zoubir Zarrouk
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Correspondence to Ahmed Ouazza.

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Ouazza, A., Rhomari, N. & Zarrouk, Z. Kernel method to estimate nonlinear structural equation models. Qual Quant 56, 3465–3480 (2022). https://doi.org/10.1007/s11135-021-01274-9

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