Arbeitspapier

Composite indices based on partial least squares

In this paper, we compare Principal Component Analysis (PCA) and Partial Least Squares (PLS) methods to generate weights for composite indices. In this context we also consider various treatments of non-metric variables when constructing such composite indices. Using simulation studies we find that dummy coding for non-metric variables yields satisfactory performance compared to more sophisticated statistical procedures. In our applications we illustrate how PLS can generate weights that differ substantially from those obtained with PCA, increasing the composite indices' predictive performance for the outcome variable considered.

Language
Englisch

Bibliographic citation
Series: Discussion Papers ; No. 171

Classification
Wirtschaft
Statistical Simulation Methods: General
Index Numbers and Aggregation; Leading indicators
Urban, Rural, Regional, Real Estate, and Transportation Economics: Household Analysis: General
Economic Impacts of Globalization: Economic Development
Subject
Principal Component Analysis
PCA
Partial Least Squares
PLS
non-metric variables
wealth index
globalization

Event
Geistige Schöpfung
(who)
Yoon, Jisu
Klasen, Stephan
Dreher, Axel
Krivobokova, Tatyana
Event
Veröffentlichung
(who)
Georg-August-Universität Göttingen, Courant Research Centre - Poverty, Equity and Growth (CRC-PEG)
(where)
Göttingen
(when)
2015

Handle
Last update
20.09.2024, 8:21 AM CEST

Data provider

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Object type

  • Arbeitspapier

Associated

  • Yoon, Jisu
  • Klasen, Stephan
  • Dreher, Axel
  • Krivobokova, Tatyana
  • Georg-August-Universität Göttingen, Courant Research Centre - Poverty, Equity and Growth (CRC-PEG)

Time of origin

  • 2015

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