Panel equation regressions
res=ppooled_hac1(y,index,x,typvcv,win)
* y = a (nobs*nindiv x 1) matrix of all of the individual's observations vertically concatenated. This matrix must include in the first column the dependent variable, the independent variables must follow accordingly.
* index = 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 = a (nobs*nindiv x k) matrix of exogenous variables
* typvcv = 1, 2 or 3 with
- 1 "clustered" covariance matrix of Arellano (1987) recommended when T is fixed and N large but also "works" when T is large and N fixed see Hansen C. B. (2007) (reference below)
- 2 a Newey-west type (Driscoll-Kray) estimator recommended when T is large and N fixed
* win = the length of the Barlett window kernel estimator (default = automatic selection by Andrews (1991) 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('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('rsqr') = rsquared
- res('rbar') = rbar-squared
- res('f') = F-stat for the nullity of coefficients other than the constant
- res('pvaluef') = its significance level