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pfixed_mbb1

Panel equation regressions

CALLING SEQUENCE

 res=pfixed_mbb1(y,index,x,B,alpha)

PARAMETERS

Input

* 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

 

Output

* 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

DESCRIPTION

Fixed effects estimation for panel data with HAC and cross-sectional dependence robust moving-block bootstrap percentile-t confidence intervals for the coefficients. Low level function that works only with matrices.

AUTHOR

Emmanuel Michaux 2012

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