Grocer Function
NAME
dfbeta - BKW influential observation diagnostics
CALLING SEQUENCE
[results]=dfbeta(y, arg1,...,argn)
PARAMETERS
Input
-
y = either an ols results tlist or a 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:
- the string 'noprint' if the user doesn't want to print the results of the regression
or, and only if y is not an ols results tlist:
- a time series
- a real (nx1) vector
- a string equal to the name of a time series or a (nx1) real vector between quotes
Output
-
results = a results tlist with
- results('meth') = 'dfbeta'
- results('nobs') = # of observations
- results('nvar') = # of variables in x-matrix
- results('dfbeta') = df betas
- results('dffits') = df fits
- results('hatdi') = hat-matrix diagonals
- results('stud') = studentized residuals
- result('namex') = name of the x variables
- result('namey') = name of the y variable
- result('prests') = boolean indicating the presence or absence of a time series in the regression
DESCRIPTION
Computes BKW (influential observation diagnostics) dfbetas, dffits, hat-matrix, studentized residuals. Plots the corresponding results.
EXAMPLE
result = dfbeta('y','x')
This example is taken from function dfbeta_d. The example provides dfbetas, dffits, hat-matrix, studentized residuals for a regression whose endogenous variable is y and exogenous variables is a matrix (as in dfbeta_d), a vector or a ts x.
AUTHOR
Eric Dubois 2002