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fac_pca_unbal

Factor estimation with an unbalanced panel

CALLING SEQUENCE

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

PARAMETERS

Input

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

   . a time series

   . real (n x p) vector

   . a string equal to the name of a time series or of a (n x p) real vector 'stan' if the user wants to perform the analysis on standardized variables

   . 'noprint' if the user does not want to plot the results

Output

* res = a results tlist with

   - res('meth') = 'factor density via bootstrapping'

   - res('y') = the (nobs x k) matrix collecting the variables

   - res('standardization') = a boolean, indicating whether variables have been standardized

   - res('# of draws') = an integrer, the # of draws

   - res('factors') = a (nobs x n_samples) matrix, the factor density

   - res('factors percentiles') = a (nobs x 5) matrix, the percentiles (2.5, 25, 50, 75 and 97.5)

   - res('namey') = name of the variables

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

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

DESCRIPTION

Principal component Factor estimation on an unbalanced panel.

EXAMPLE

global GROCERDIR; 
load(GROCERDIR+'\data\esp_yields_unb.dat')
bounds()
// estimates the factor after ahvaing standardized data
res = fac_pca_unbal(esp_yields_unb,'stan');

AUTHOR

Éric Dubois 2022

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