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olsar1_1

maximum likelihood estimation of an autocorrelated model

CALLING SEQUENCE

[result]=olsar1_1(y,x,optfunc,opt_optim)

PARAMETERS

Input

* y = a real (n,1) vector or a

* x = a real (n,k) matrix

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

* opt_optim = a tlist, collecting the options to the optimisation function

 

Output

* rolsar1 = a results tlist with

  - rolsar1('meth') = ' ar(1) maximum likelihood'

  - rolsar1('y') = y data vector

  - rolsar1('x') = x data matrix

  - rolsar1('nobs') = # observations

  - rolsar1('nvar') = # variables

  - rolsar1('beta') = bhat

  - rolsar1('yhat') = yhat

  - rolsar1('resid') = residuals

  - rolsar1('vcovar') = estimated variance-covariance matrix of beta

  - rolsar1('sige') = estimated variance of the residuals

  - rolsar1('sigu') = sum of squared residuals

  - rolsar1('ser') = standard error of the regression

  - rolsar1('tstat') = t-stats

  - rolsar1('pvalue') = pvalue of the betas

  - rolsar1('dw') = Durbin-Watson Statistic

  - rolsar1('condindex') = multicolinearity cond index

  - rolsar1('prescte') = boolean indicating the presence or absence of a constant in the regression

  - rolsar1('rsqr') = rsquared

  - rolsar1('rbar') = rbar-squared

  - rolsar1('f') = F-stat for the nullity of coefficients other than the constant

  - rolsar1('pvaluef') = its significance level

  - rolsar1('prests') = boolean indicating the presence or absence of a time series in the regression

  - rolsar1('rho') = estimated first order autocorrelation of residuals

  - rolsar1('trho') = its Student t

  - rolsar1('like') = log-likelihood of the regression

DESCRIPTION

Computes maximum likelihood ols regression for AR1 errors.

EXAMPLE

x=grand(15,3,'nor',0,1)
y=x*ones(3,1)+autocum(grand(15,1,'nor',0,1),0.8)
rolsar1=olsar1_1(y,x)

AUTHOR

Eric Dubois 2005

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