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prandom

Random Effects Estimation for Panel Data

CALLING SEQUENCE

res=prandom(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)

* arg1=

  - 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

* arg2,...,argn=

  - if first input of varargin was a 'panel data' tlist then other input are optional and can be:

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

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

    . or the string 'glsmeth=n' where n is the name of a method available to estimate the gls parameters, that is 'wallace', 'swamy', 'amemiya' or 'nerlove' (default: 'swamy')

  - if first input of varargin was an endogenous variable then:

    . either a time series

    . or a real (nxk) matrix

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

    . or 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)

    . or the string 'glsmeth=n' where n is the name of a method available to estimate the gls parameters, that is 'wallace', 'swamy', 'amemiya' or 'nerlove' (default: 'swamy')

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

 

Output

* res = a results 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('sigu') = sum of squared 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

   - res('gls estimation method') = the gls method used

   - res('random effects') = the estimation of the individual effects

   - res('res0') = residuals from the original model

   - res('alfa') = the gls parameters

   - res('prests') = boolean indicating the presence or absence of a time series in the regression

   - res('namey') = name of the y variable

   - res('namex') = name of the x variables

DESCRIPTION

Performs Random Effects Estimation for Panel Data(for balanced or unbalanced data).

EXAMPLE

load(GROCERDIR+'\macros\grocer\db\judgepanel.dat') ;
r2 = prandom('y',judgepanel);
//Example taken from function panel_d. Provides random panel estimation on Judge et alii example. GLS estimation method is the default one, that is 'swamy'.
 
load(GROCERDIR+'\macros\grocer\db\judgepanel.dat') ;
r2 = prandom('y',judgepanel,'glsmeth=wallace');
// provides random panel estimation on Judge et alii example, but with GLS estimation method set to 'wallace'.

AUTHOR

Eric Dubois 2005

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