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pcce

Common correlated effects estimators

CALLING SEQUENCE

res=pcce(namey,arg1,…,argn)

PARAMETERS

Input

• namey = a real (nx1) vector or a string equal to the name of a time series or a (nx1) real vector between quotes (this last case is the only one authorized if you are using a 'panel data' tlist, see below)

• first input of arg1,…,argn:

  - either a 'panel data' tlist (generally imported from a .csv database by function impexc2bd)

  - or an endogenous variable taking the form of a time series, a real (nx1) vector or a string equal to the name of a time series or a (nx1) real vector between quotes

• other input of arg1,…,argn:

  - if first input of arg1,…,argn was a 'panel data' tlist then: other input are optional and can be:

• 'x=name1;…;namep' where name1,…,namep are a subset of the names of the variables that are in the database

• the string 'nameid=name1,…, namen' where name1,… are names of individuals present in the database

• 'yar=namear' the name of the AR endogeneous variable in case of a dynamic panel data model that is in the database

  - if first input of arg1,…,argn was an endegnous variable then either:

• a time series

• a real (nxk) matrix

• a (kx1) string vector of names of time series, vectors or matrices

• the string 'id=v' where v is the vector of individuals attached to the y and x data (this argument must be present somewhere in the list of variables arguments)

• 'yar=namear' the name of the AR endogeneous variable in case of a dynamic panel data model

  - 'cce=xxx' type of common correlated effect estimators (dynamic or static, depending on the presence of an AR part)

• 'meang' for the mean group (CCEMG)

• 'pooled' for the pooled one (CCCEP)

  - 'jackkn' if the user wants a half-panel jackknife correction for CCEMG estimator with dynamic panel

  - 'wvec = xxx' a vector of individual weights for the CCEP estimator (optional, default = 1/#individuals)

  - 'mglag = xxx' number of lags to filter residuals case of a CCEMG dynamic panel estimation (default int(T^(1/3)))

  - the string 'noprint' if the user doesn't want the to print the results of the regression

Output

• res = a tlist with

• res('meth')='panel with random 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('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

DESCRIPTION

Pesaran's et alii common correlated effects estimators for static and dynamic heterogeneous panel data models.

EXAMPLE

// mean group estimator
rp1=pcce('dlxrate',pdb,'x=[money_s;rmmkt_s;pic_s]','cce=meang');
// pooled estimator
rp2=pcce('dlxrate',pdb,'x=[money_s;rmmkt_s;pic_s]','cce=pooled');
// mean group estimator with an AR part
rp3=pcce('dlxrate',pdb,'x=[money_s;rmmkt_s;pic_s]','yar=dlxrate1','cce=meang');
// mean group estimator with an AR part and 4 lags for the cross-sectionnal means of the regressors (to "whiten" residuals)
rp4=pcce('dlxrate',pdb,'x=[money_s;rmmkt_s;pic_s]','yar=dlxrate1','cce=meang','mglag=12');
// with an AR part, 4 lags for the cross-sectionnal means of the regressors and an half-panel jackknife correction
rp5=pcce('dlxrate',pdb,'x=[money_s;rmmkt_s;pic_s]','yar=dlxrate1','cce=meang','jackkn');

AUTHOR

Emmanuel Michaux 2013

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