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Grocer >> Bayesian Model Averaging > bma_g

bma_g

Bayesian model averaging

CALLING SEQUENCE

rbma_g = bma_g(namey,ndraws,arg1,...,argn)

PARAMETERS

Input

* namey = 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

* ndraws = # of draws

* argi = arguments which can be:

  - a time series

  - a real (nx1) vector

  - a real (nxk) matrix

  - a string equal to the name of a time series or a (nxk) real vector or matrix between quotes

  - a list of such elements

  - 'nvmax=xx' maximum number of variable allowed in each model

  - 'burnin=xx' number of burn-in MCMC simulation

  - 'g =XX' value of g-prior (default = 1/max(n,k^2))

  - 'mcmc = ''mc3'' or ''jump''' type of MCMC algorithm (MC3 or reversible jump) must be in quote

  - 'nvmax=xx' maximum number of variable allowed in each model

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

 

Output

* rbma_g = a results tlist with

  -rbma_g('meth') = 'bma g-prior'

  -rbma_g('nmod') = # of models visited during sampling

  -rbma_g('beta') = bhat averaged over all models

  -rbma_g('mprob') = posterior prob of each model

  -rbma_g('vprob') = posterior prob of each variable

  -rbma_g('model') = indicator variables for each model (nmod x k)

  -rbma_g('yhat') = yhat averaged over all models

  -rbma_g('resid') = residuals based on yhat averaged over models

  -rbma_g('sige') = averaged over all models

  -rbma_g('nobs') = nobs

  -rbma_g('nvar') = # of exogenous

  -rbma_g('y') = y data vector

  -rbma_g('x') = y data vector

  -rbma_g('visit') = visits to each model during sampling (nmod x 1)

  -rbma_g('time') = time taken for MCMC sampling

  -rbma_g('ndraw') = # of MCMC sampling draws

  -rbma_g('burnin')= # of burn-in MCMC simulation

  -rbma_g('gprior')= value of g-prior

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

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

  -rbma_g('bounds')= if there is a timeseries in the regression, the bounds of the regression

  -rbma_g('mcmc') = type of MCMC algorithm

DESCRIPTION

Computes bayesian model averaging under g-prior with selection of g-prior as proposed by Fernadez et alii (2001) g = 1/max(n,k^2)

EXAMPLE

load(GROCERDIR+'/data/crime.dat')
 
// take log of data
listn = ['crime';'m';'ed';'po1';'po2';'lf';'mf';'pop';'nw';'u1';'u2';'gdp';'ineq';'prob';'time'] ;
for i =1:size(listn,1)
    execstr('l'+listn(i)+' = log('+listn(i)+')')
end
 
// BMA estimation
rbma = bma_g('lcrime',30000,'lm','so','led','lpo1','lpo2','llf','lmf','lpop','lnw','lu1','lu2',...
'lgdp','lineq','lprob','ltime','burnin=10000','mcmc=''mc3''');
 
// Example taken from bma_d()

AUTHOR

Emmanuel Michaux 2006

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