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pdccemg_jkn1

Mean group common correlated effects estimator with half-panel jackknife correction for dynamic models

CALLING SEQUENCE

 rp=pdccemg_jkn1(y,index,yar,x,lg)

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.

* yar = a (nobs*nindiv x 1) vector of lagged (AR(1)) endogeneous variable

* 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 = (nobs*nindinv x k) matrix of exogeneous variables

* lg = lag of cross-sectional means for dynamic panel estimation (default int(T^(1/3)))

 

Output

* res = a tlist with

  - rp('meth') = 'CCEMG'

  - rp('y') = y data vector

  - rp('x') = x data matrix

  - rp('nobs') = nobs

  - rp('nvar') = nvars

  - rp('beta') = bhat

  - rp('betai') = individual regression coefficients

  - rp('w') = vector of individual weights

  - rp('yhat') = yhat

  - rp('resid') = residuals

  - rp('vcovar') = estimated variance-covariance matrix of the CCCEP estimators

  - rp('sige') = estimated variance of the residuals

  - rp('sigu') = sum of squared residuals

  - rp('ser') = standard error of the regression

  - rp('tstat') = t-stats (CCEP estimators and fixed effects)

  - rp('pvalue') = pvalue of the betas

  - rp('condindex') = multicolinearity cond index

  - rp('prescte') = boolean indicating the presence or absence of a constant in the regression

DESCRIPTION

Pesaran and Chudik common correlated effects mean group estimator with the half-panel jackknife correction for heterogeneous dynamic panel data models.

AUTHOR

Emmanuel Michaux 2013

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