Cochrane-Orcutt estimation of an autocorrelated model
rolsc=olsc(namey, arg1,...,argn)
* 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
* argi = an argument 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
- the string 'noprint' if the user doesn't want to display the results of the regression
- the string 'maxit=n' where n is the maximum number of iterations that can be performed (default = 100)
- the string 'crit=n' where crit is the convergence criterion, used to assess if the difference between successive values of the autocorrelation coefficient is significant (default=sqrt(%eps))
* rolsc = a results tlist with
- rolsc('meth') = 'Cochrane-Orcutt'
- rolsc('y') = y data vector
- rolsc('x') = x data matrix
- rolsc('nobs') = # observations
- rolsc('nvar') = # variables
- rolsc('beta') = bhat
- rolsc('yhat') = yhat
- rolsc('resid') = residuals
- rolsc('vcovar') = estimated variance-covariance matrix of beta
- rolsc('sige') = estimated variance of the residuals
- rolsc('sigu') = sum of squared residuals
- rolsc('ser') = standard error of the regression
- rolsc('tstat') = t-stats
- rolsc('pvalue') = pvalue of the betas
- rolsc('dw') = Durbin-Watson Statistic
- rolsc('condindex') = multicolinearity cond index
- rolsc('prescte') = boolean indicating the presence or absence of a constant in the regression
- rolsc('rsqr') = rsquared
- rolsc('rbar') = rbar-squared
- rolsc('f') = F-stat for the nullity of coefficients other than the constant
- rolsc('pvaluef') = its significance level
- rolsc('prests') = boolean indicating the presence or absence of a time series in the regression
- rolsc('namey') = name of the y variable
- rolsc('namex') = name of the x variables
- rolsc('bounds') = if there is a timeseries in the regression, the bounds of the regression
- rolsc('rho') = estimated first order autocorrelation of residuals
- rolsc('trho') = its Student t
- rolsc('iterout') = a (niter x 3) matrix giving for each
load('grocer/bdexamples/bdhenderic.dat') ; bounds('1964q3','1989q2') ; rolsc=olsc('delts(lm1-lp)','delts(lp)','rnet','lagts(1,lm1-lp-ly)','cte') // This example, taken from olsar1_d, shows the estimation of first order autocorrelated model on Hendry and Ericsson (1991) preferred specification // from which the variable 'delts(lagts(1,lm1-lp-ly))' has been withdrawn. | ![]() | ![]() |