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fac_pca

Static factor analysis

CALLING SEQUENCE

res=fac_pca(arg1,...,argn)

PARAMETERS

Input

* arg1,...,argn = arguments which can be:

  - time series

  - a real (nxp) vector

  - a string equal to the name of a time series or a (nxp) real vector between quotes

  - 'bai_ng     =xx' if the user want to determine the number of factors with Bai-Ng information criteria (ICp1,ICp2 or ICp3)

  - 'snum==xx' a vector with the factors to keep (e.g [1,3,...,k]) or the maxiumum number of factors to test (here k) if bai_ng option is selected

  - 'pnum==xx' number of factors to keep for results printing

  - the string 'noprint' if the user doesn't want to print the results of the regression

 

Output

* res = a results tlist with

  - res('namex') = vector of names of the x variables

  - res('namex') = vector of names of the x variables

  - res('x') = matrix of x data

  - res('fi') = first factor selected by the user (in TS format when working with TS) it's the factor i i.e the i-th most important contributor to total variance

  - res('fj') = second factor selected by the user it's the factor j i.e the j-th most important contributor to total variance and so on

  - res('propor') = proportion of variance explained by each factor

  - res('corr_x_f') = matrix of correlations between variables and factors default is the first five factors)

  - res('loadings') = matrx of loadings (variables are in rows) (default is the first five factors)

  - res('snum') = number of selected factor by the user of Bai-Ng (default is the first factor)

  - res('pnum') = number of selected factor for printing results (default is the first five factors)

  - res('bai_ng')= 'no','ICp1','ICp2', 'ICp3'

  - res('ICpk') = value of information criteria selected for each of the number of factor tested

  - res('prests') = boolean indicating the presence or absence of a time series

  - res('bounds')= if there is a timeseries in the analysis, the bounds of the regression

DESCRIPTION

Performs the estimation of a Static factor analysis with the selection of the number of factors according to one of the information criteria proposed by Bai-Ng. NOTE: can also be used as an approximate dynamic factor analysis (see Stock, J. H. and M. Watson (1998), "Diffusion Indexes", NBER Working Paper, n. 6702)

EXAMPLE

load(GROCERDIR+'/data/BusinessSurvey.dat');
 
res = fac_pca('pp','fp','gpp','gob','fob','in','pnum=6','snum=1');
// Example taken from fac_pca_d():

AUTHOR

Emmanuel Michaux 2006

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