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ppooled

Panel equation regressions

CALLING SEQUENCE

res=ppooled(namey,arg1,...,argn)

PARAMETERS

Input

* namey = a real (nx1) vector or a string equal to the name of a time series or a (nx1) real vector between quotes (this last case is the only one authorized if you are using a 'panel data' tlist, see below)

* arg1 =

  - either a 'panel data' tlist (generally imported from a .csv database by function impexc2bd)

  - or an endogenous variable taking the form of a time series, a real (nx1) vector or a string equal to the name of a time series or a (nx1) real vector between quotes

* arg2,...,argn=

  - if first input of arg1,...,argn was a 'panel data' tlist then: other input are optional and can be:

    . 'x = name1;...;namep' where name1,...,namep are a subset of the names of the variables that are in the database

    . 'cte' if the user does not want to add a constant to the regression

    . the string 'noprint' if the user does not want to print the estimation results

  - if first input of arg1,...,argn was an endogenous variable then either:

    . a time series

    . a real (n x k) matrix

    . a (k x 1) string vector of names of time series, vectors or matrices

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

  - 'hac=ccm' for "clustered" covariance matrix of Arellano (1987) recommended when T is fixed and N large but "works" also when T is large and N fixed see Hansen C. B. (2007) (reference below) or 'hac=nw' for a Newey-west type (Driscoll-Kraay) estimator recommended when T is large and N fixed In that cases the string 'id=v' a (T x 1) index vector that identifies each observation with an individualmust be given somewhere when calling the function. It can be in the panel tlist or an argument of the function

  - 'win=n' the length of the Barlett window kernel estimator (default = automatic selection by Andrews (1991) method using an AR(1) model)

 

Output

* res = a tlist with

  - res('meth') = 'panel pooled'

  - res('y') = y data vector

  - res('x') = x data matrix

  - res('nobs') = nobs

  - res('nvar') = nvars

  - res('beta') = bhat

  - res('yhat') = yhat

  - res('resid') = residuals

  - res('vcovar') = estimated variance-covariance matrix of beta

  - res('sige') = estimated variance of the residuals

  - res('sigu') = sum of squared residuals

  - res('ser') = standard error of the regression

  - res('tstat') = t-stats

  - res('pvalue') = pvalue of the betas

  - res('condindex') = multicolinearity cond index

  - res('prescte') = boolean indicating the presence or absence of a constant in the regression

  - res('rsqr') = rsquared

  - res('rbar') = rbar-squared

  - res('f') = F-stat for the nullity of coefficients other than the constant

  - res('pvaluef') = its significance level

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

  - res('namey') = name of the y variable

  - res('namex') = name of the x variables

DESCRIPTION

Performs HAC or standard pooled effect estimation for panel data (for balanced or unbalanced data).

EXAMPLE

load(GROCERDIR+'\macros\grocer\db\judgepanel.dat') ;
r = ppooled('y',judgepanel);
// Example is taken from function panel_d. Provides fixed panel estimation on Judge et alii example.

AUTHOR

Eric Dubois 2005 and Emmanuel Michaux 2012

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