Grocer Function
NAME
cusumb - backward cusum stability tests
CALLING SEQUENCE
[rcusum]=cusumb(y,arg1,...,argn)
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
- argi = 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
- the string 'noprint' if the user doesn't want to print the results of the regression
- the string 'size=x' where x=0.01, 0.05 or 0.1 is the size chosen for the test (default =0.05)
Output
-
rcusum = a results tlist with:
- rcusum('meth')='cusum'
- rcusum('nobs')= # of observations
- rcusum('nvar')= # of variables
- rcusum('y')= y data vector
- rcusum('x')= x matrix vector
- rcusum('rres')= vector of recursive residuals
- rcusum('cusum')= cusum test
- rcusum('cusum_l90')= the lower value of its 90% confidence interval
- rcusum('cusum_u90')= the upper value of its 90% confidence interval
- rcusum('cusum_l95')= the lower value of its 95% confidence interval
- rcusum('cusum_u95')= the upper value of its 95% confidence interval
- rcusum('cusum_l99')= the lower value of the 99% confidence interval
- rcusum('cusum_u99')= the upper value of the 99% confidence interval
- rcusum('cusums')= squared cusum test
- rcusum('cusums_l90')= the lower value of its 90% confidence interval
- rcusum('cusums_u90')= the upper value of its 90% confidence interval
- rcusum('cusums_l95')= the lower value of its 95% confidence interval
- rcusum('cusums_u95')= the upper value of its 95% confidence interval
- rcusum('cusums_l99')= the lower value of the 99% confidence interval
- rcusum('cusums_u99')= the upper value of the 99% confidence interval
- rcusum('prests')=boolean indicating the presence or absence of a time series in the regression
- rcusum('namey') = name of the y variable
- rcusum('namex') = name of the x variables
- rcusum('bounds') = if there is a timeseries in the regression, the bounds of the test (which are the bounds of the regression, less the k first dates)
DESCRIPTION
Computes backward cusum and cusum-squares test and plots the tests values along with their 5% confidence bands (see Brown R.L, J. Durbin and J.M Evans (1975):"Techniques for Testing the Constancy of Regression Relationship over Time", Journal of the Royal Statistical Society, Series B, n°2, 149-192).
EXAMPLE
load('grocer/bdexamples/bdhenderic.dat') ; bounds('1964q3','1985q2') ; r=cusumb('del(lm1-lp)','del(lp)','del(lagts(1,lm1-lp-ly))','rnet','lagts(1,lm1-lp-ly)','cte')
This example is taken from function cusum_d. The example provides the cusum backward stability test for the estimation of Hendry and Ericsson equation # 6.
AUTHOR
Eric Dubois 2002