Grocer Function
NAME
tobit - Tobit estimation
CALLING SEQUENCE
[rtobit]=tobit(grocer_namey,varargin)
PARAMETERS
Input
-
grocer_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
-
varargin = arguments which can be:
. a time series
. a real (nx1) vector
. a string equal to the name of a time series or a (nx1) real vector between quotes
. a string option which can be:
- 'trunc=left' or 'trunc=right' for censoring (default=left)
- 'vtrunc=x' where x is the value for censoring (default=0)
- 'b0=x' where x is the starting values for parameters (default = ols)
- 'hess=x' where x = Hessian: 'dfp', 'bfgs', 'gn', 'marq', 'sd' (default = 'bfgs')
- 'btol=x' where x is the tolerance for b convergence (default = 1e-8)
- 'ftol=x' tolerance for FUN convergence (default = 1e-8)
- 'maxit=x' is the maximum # of iterations (default = 500)
Output
-
rtobit = a results tlist with
. rtobit('meth') = 'tobit'
. rtobit('y') = y data vector
. rtobit('x') = x data matrix
. rtobit('nobs') = # observations
. rtobit('nobsc') = # censored observations
. rtobit('nvar') = # variables
. rtobit('beta') = bhat
. rtobit('yhat') = yhat
. rtobit('resid') = residuals
. rtobit('vcovar') = estimated variance-covariance matrix
of beta
. rtobit('sige') = estimated variance of the residuals
. rtobit('sigu') = sum of squared residuals
. rtobit('ser') = standard error of the regression
. rtobit('tstat') = t-stats
. rtobit('pvalue') = pvalue of the betas
. rtobit('dw') = Durbin-Watson Statistic
. rtobit('condindex') = multicolinearity cond index
. rtobit('prescte') = %f = boolean indicating the absence
of a constant in the regression
. rtobit('iter') = # iterations performed
. rtobit('llike') = log likelihood
. rtobit('opthess') = option used to update hessian
. rtobit('grad') = gradient at the optimum
. rtobit('ts') = boolean indicating the presence or
absence of a time series in the regression
. rtobit('namey') = name of the y variable
. rtobit('namex') = name of the x variables
. rtobit('bounds') = if there is a timeseries in the regression, the bounds of the regression
DESCRIPTION
Computes Tobit Regression. If the user has not given the argument 'noprint', the results of the regression and various diagnostics.
EXAMPLE
tobit('y','x','hess=dfp','maxit=1000');
Example taken from tobit_d. The endogenous varaible is y, x is a matrix of exogenous variables. Hessain calcualtion method is dfp and maximum
# of iterations is set to 1000.
AUTHOR
Eric Dubois 2002