Panel equation regressions
res=pfixed_mbb1(y,index,x,B,alpha)
* 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
* mol = moving blocks length
* B = number of bootstrap replications
* alpha = confidence level
* res = a tlist with
-res('meth') = 'panel with fixed effects'
-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('lliked') = 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
-res('hac') = type of robust variance matrix in case of HAC estimation