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Joreskog, K.G. and Wold, H. (1982) The ML and PLS Techniques for Modeling with Latent Variables: Historical and Comparative Aspects. In: Joreskog, K.G. and Wold, H., Eds., Systems under Indirect Observation: Causality, Structure, Prediction, Part I, Elsevier, Amsterdam, 263-270.

has been cited by the following article:

• TITLE: Explaining Systemic Risk in Money Market Funds

  AUTHORS: Necmi K. Avkiran

  KEYWORDS: Systemic Risk, Institutional Prime Money Market Funds, Latent Variables, Partial Least Squares Structural Equation Modeling

  JOURNAL NAME: Theoretical Economics Letters, Vol.8 No.9, June 12, 2018

  ABSTRACT: For the first time, this study evaluates the contributions to systemic risk in the context of U.S. institutional prime money market funds (MMFs) from different sources using partial least squares structural equation modeling (PLS-SEM). The primary motivation behind this study is to trace systemic risk to its underlying sources and measure which types of relationships provide significant explanation using PLS-SEM. I illustrate the application of PLS-SEM and interpretation of results in a step-by-step manner to empower those new to PLS-SEM, and undertake robustness testing. Findings indicate that through crisis years, macroprudential indicators contribute to potential systemic risk more than prudential indicators. This suggests that macroprudential indicators that can be traced to individual MMFs market positions are more important in understanding systemic risk during crises, and further underlines the interconnectedness of markets. PLS-SEM can be used to test the explanatory power of new indicators as they emerge in an exploratory environment.