<< ppooled Panel equation regressions ppooled_hac1 >>

Grocer >> Panel equation regressions > ppooled1

ppooled1

Pooled Estimation for Panel Data

CALLING SEQUENCE

res=ppooled1(y,index,x,z)

PARAMETERS

Input

* y = a (nobs x 1) vector of endogenous variable

* index = a (nobs x 1) index vector that identifies each observation with an individual

  e.g. 1 (first 2 observations for individual # 1)

    1

    2 (next 1 observation for individual # 2)

    3 (next 3 observations for individual # 3)

    3

    3

* x = matrix of exogenous variables

* z = optional matrix of exogenous variables, dummy variables.

 

Output

* res = a results tlist with:

  - res('meth') ='pooled panel'

  - 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('sigu') = sum of squared residuals

  - res('sige') = estimated variance of the 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('llike') = log-likelihood

  - 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

DESCRIPTION

Performs pooled Estimation for Panel Data (for balanced or unbalanced data). Low level function that works only with matrices.

EXAMPLE

load(GROCERDIR+'/data\judgepanel.dat') ;
res=ppooled1(judgepanel('x')(:,1),[judgepanel('x')(:,2) 0*judgepanel('x')(:,1)+1])
// provides the pooled estimation on Judge panel data, taking directly the matrices in the panel tlist

AUTHOR

Eric Dubois 2005

Report an issue
<< ppooled Panel equation regressions ppooled_hac1 >>