<< olsar1_1 Single equation regressions olsarma1 >>

Grocer >> Single equation regressions > olsarma

olsarma

ordinary least squares with ARMA errors

CALLING SEQUENCE

res=olsarma1(AR,MA,y,x,initown,namexos,bhat)

PARAMETERS

Input

* AR = a (nar x 1) or (1 x nar) string or real vector of parameters corresponding to the AR part of the error process

  - if AR is a real then all parameters are estimated

  - if AR is a string then all parameters with in AR with an equality (such as '=0.5') are constrained to the given value (0.5 in the example)

  - if AR is a string then it can contain inequality constraints; for instance '<0.5' indicates that coeff must be lower than 0.5

  - if initown is set to %F, then the user can give any value to AR; only it size matters for the estimation process

  - if initown is set to %F,

* MA = a (nmaf x 1) or (1 x nmaf) string or real vector of corresponding to the AR part of the error, with the same working as for AR

* 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

* arg1,...,argn = arguments which can be:

  - a time series

  - a real (nxp) vector

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

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

  - 'dropna' if the user wants to remove the NA values from the data

  - 'init=own' if the user wants the function to impose starting values for the parameters

  - 'beta=xxx' to fix the starting values of the coefficients of the regression if the user has given the option 'init=own'

  - 'optfunc=optim' if the user wants to use the optim optimisation function (default: optimg)

  - 'opt_nelmead=crit,nitermax' with crit the value of the convergence criterion in the Nelder-Meade optimisation function and nitermax the maximum number of iterations (default = 'opt_nelmead=2*%eps,1000')

  - 'opt_optim=opts' where opts are options for optim that can be entered after the starting value of the parameters (default = 'opt_optim=,''ar'',1e6,1e6'')

  - 'opt_convg=val' where val is the threshold on gradient norm (default = 'opt_convg=1e-5')

 

Output

* res = a results tlist with

  - res('meth') = 'ols with arma errors'

  - res('y') = y data vector

  - res('x') = x data matrix

  - res('nobs') = # observations

  - res('nvar') = # variables

  - 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('sigu') = sum of squared residuals

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

  - res('tstat') = t-stats

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

  - res('dw') = Durbin-Watson Statistic

  - res('condindex') = multicolinearity cond index

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

  - res('llike') = the log-likelihood

  - res('AR') = the estimated AR part of the residuals

  - res('MA') = the estimated MA part of the residuals

  - res('tAR') = the t-statistics of the AR part of the residuals

  - res('tMA') = the t-statistics of the MA part of the residuals

  - res('pvalues AR') = the p-values of the AR part of the residuals

  - res('pvalues MA') = the p-values of the MA part of the residuals

  - res('V') = the estimated variance of the innovations of the residuals

  - res('AIC') = the value of the Akaïke Critrium

  - res('BIC') = the value of the Schwarz Critrium

  - res('grad') = the gradient at solution

  - res('type') = the e4 type of the model

  - 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

  - res('dropna') = boolean indicating if NAs have been dropped

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

  - res('nonna') = vector indicating position of non-NAs

DESCRIPTION

Performs the estimation of an ols model with arma errors.

EXAMPLE

1) r=olsarma([0 0],0,'y','x')
2) r=olsarma(['=0.5' '0'],'-0.9&lt;0.9','y','x1','x2','x3')
 
// Example 1 estimates the model y=x*b+U where U follows an ARMA(2,1) model.
// Example 2 performs the regression of y on x1, x2, x3 with errors following an ARMA(2,1) model, the first AR coefficient constrained to 0.5 and the MA coefficient constrained to lie between -0.9 and 0.9.

AUTHOR

Eric Dubois 2010

Report an issue
<< olsar1_1 Single equation regressions olsarma1 >>