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PCA2: Stata module to apply Principal Component Analisys (PCA) to standard and GMM-style instrumental variables

Author

Listed:
  • Maria Elena Bontempi

    (University of Bologna)

  • Irene Mammi

    (University of Bologna)

Programming Language Stata Abstract
pca2 applies the Principal Component Analysis (PCA) to a set of different variables, or to a set of GMM-style lags of the same variable, or to a set of lags of different variables. PCA is aimed at data reduction and consists of an eigenvalue-eigenvector decomposition of the correlation or covariance matrix of the variables in order to obtain a set of orthogonal linear combinations of the original variables that account for most of the variability in the original data.

Suggested Citation

  • Maria Elena Bontempi & Irene Mammi, 2015. "PCA2: Stata module to apply Principal Component Analisys (PCA) to standard and GMM-style instrumental variables," Statistical Software Components S458115, Boston College Department of Economics.
    • Handle: RePEc:boc:bocode:s458115
    Note: This module should be installed from within Stata by typing "ssc install pca2". The module is made available under terms of the GPL v3 (https://www.gnu.org/licenses/gpl-3.0.txt). Windows users should not attempt to download these files with a web browser.
    as

    Download full text from publisher

    File URL: http://fmwww.bc.edu/repec/bocode/p/pca2.ado
    File Function: program code
    Download Restriction: no
    File URL: http://fmwww.bc.edu/repec/bocode/p/pca2.hlp
    File Function: help file
    Download Restriction: no
    File URL: http://fmwww.bc.edu/repec/bocode/p/pca2_table1.do
    File Function: sample do-file
    Download Restriction: no
    File URL: http://fmwww.bc.edu/repec/bocode/p/pca2_table1.dta
    File Function: sample data file
    Download Restriction: no
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