Grocer Function
NAME
varma - estimation of a (V)ARMA(X) model
CALLING SEQUENCE
result=varma(endo,AR,ARS,MA,MAS,v,s,varargin)
PARAMETERS
Input
- endo = either
- a (Txn) real matrix
- a matrix of strings, each one being the name of a variable
- a timeseries
- a list of variables; each element could be a timeseries, a real vector, a real matrix or a string (the name of a variable with
one of the types cited above, between quotes)
-
AR = the matrix [] or a (nx(n*p)) matrix
with:
- n = # of endogenous variables in grocer_e4_vari
- p = # of lags in the AR part of the process
-
ARS = the matrix [] or a (nx(n*ps)) matrix
with:
- ps = # of lags in the seasonal AR part of the process
-
MA = the matrix [] or a (nx(n*q)) matrix
with:
- q = # of lags in the AM part of the process
-
MAS = the matrix [] or a (nx(n*qs)) matrix x
with:
- qs = # of lags in the seasonal MA part of the process
-
v = a (nx1) vector if the user wants to impose independence between resisduals or a (nxn) matrix in the other case
-
s = a scalar representing the seasonality
Output
-
result = a results tlist with:
- result('meth') ='varma'
- result('y') = (nobsxnendo) matrix of values for the endogenous variables
- result('namey') = (nvarx1) vector of names for the endogenous variables
- result('nobs') = # of observations
- result('coeff') = (npx1) vector of estimated parameters
- result('lab') = (npx1) string vector of names for the estimated parameters
- result('llike') = log-likelihood of the model
- result('tstat') = Student's t of the coefficients
- result('std') = (npx1) Student's t of the coefficients
- result('corr') = (npxnp) correlation matrix
- result('AIC') = Akaike information criterion
- result('BIC') = Schwartz information criterion
- result('theta2mat') = (npx1) string vector making the transformation of the vector of estimated parameters into the matrices of the problem
- result('seas') = order of the seasonality
- result('nexo') = # of exogenous variables in the model
- result('resid') = (nobsx1) vector of filtered residuals
DESCRIPTION
Estimates a VARMA model using E4 functions the ARMA model has the following form:
AR(L)*ARS(L^s) y = MA(L)*MAS(L^s) e [+G(L)X]
where L is the lag operator, X is an optional vector of exogenous variables
EXAMPLE
mtlb_load('SCI/scied/grocer/encours/e4sci/seriesa.dat'); elec_cons = transdif(seriesa,0,1,1,12);
results=varma(elec_cons,[],[],0,0,0,12)
Provides the estimation of a VARMA model without AR part and without exogenous variables (in particular without constant)
Series are monthly and starting values for the 3 estimated parameters (the first MA parameter, the first
seasonal MA and the variance of residuals) are all 0.
AUTHOR
Eric Dubois 2004