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Treatments of Non-metric Variables in Partial Least Squares and Principal Component Analysis

Author

Listed:
  • Jisu Yoon

    (Georg-August-University Göttingen)

  • Tatyana Krivobokova

    (Georg-August-University Göttingen)

Abstract

This paper reviews various treatments of non-metric variables in Partial Least Squares (PLS) and Principal Component Analysis (PCA) algorithms. The performance of different treatments is compared in the extensive simulation study under several typical data generating processes and recommendations are made. An application of PLS and PCA algorithms with non-metric variables to the generation of a wealth index is considered.

Suggested Citation

  • Jisu Yoon & Tatyana Krivobokova, 2015. "Treatments of Non-metric Variables in Partial Least Squares and Principal Component Analysis," Courant Research Centre: Poverty, Equity and Growth - Discussion Papers 172, Courant Research Centre PEG.
    • Handle: RePEc:got:gotcrc:172
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    File URL: http://www2.vwl.wiso.uni-goettingen.de/courant-papers/CRC-PEG_DP_172.pdf
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    More about this item

    Keywords

    Principal Component Analysis; PCA; Partial Least Squares; PLS; non-metric variables; simulation; wealth index;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • R20 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - General

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