Panel equation regressions
res=ppooled(namey,arg1,...,argn)
* 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)
* 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
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. | ![]() | ![]() |