Grocer Function
NAME
automatic - automatic general to specific regression
CALLING SEQUENCE
[results]=automatic(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 (nxp) vector
. a string equal to the name of a time series or a (nx1) real vector between quotes
. the string 'prt=opt1,opt2,...,optn'
where opti is one of the following available options:
- nothing (nothing printed !)
- initial (results of initial model printed)
- st0_mod (results of stage 0 model printed)
- st1_mod (results of stage 1 models printed)
- st1_union (results of stage 1 union model printed)
- st2_mod (results of stage 2 models printed)
- st2_union (results of stage 2 union model printed)
- final (results of final model printed)
- test_inter (results of specification tests for the intermediate results printed)
- test_final (results of specification tests for the final model printed)
- test (specification tests printed for every regression result printed)
- st1_path (results of stage 1 paths printed)
- st2_path (results of stage 2 paths printed)
- all (all results printed: can be very long !)
. the string 'test=opt1,opt2,...,optn'
where opti is one of the following available options:
- chowtest(p)
- predfailin(p)
- doornhans
- arlm(p)
- hetero_sq
. the string 'alpha=p' for the simplification significance level (default = 0.01)
. the string 'f0_sig=p' for the pre-test (default = 0.9)
. the string 'eta=p' (default = 0.01)
. the string 'gam=p' (default = 0.05)
Output
-
a results tlist with the following fields:
- results('meth') = 'automatic';
- results('f_test') = the function used to perform the specification tests
- results('ending reason') = the reason why the final model has been chosen
- results('initial model') = the estimation results of the unrestricted model
- results('stage 0 model') = the estimation results of the stage 0 restricted model (all variables whose individual tstat level is lower than 1 and whose joint significance level is lower than f0_sig are withdrawn)
- results('final model') = the estimation results of the final model
- results('stage i models') = the estimation results of the stage i (i=1,2) models and the corresponding paths
- results('stage i union model') = the estimation results of the model built from the union of stage i (i=1,2) models
DESCRIPTION
The implementation of Krolzig, H.-M. and Hendry, D.F. (2001): "Computer Automation of General-to-Specific Model Selection Procedures", Journal of Economic Dynamics and Control, 25 (6-7), 831-866. The program selections a model by least-squares regressions: starting from the list of exogenous variables provided by the user, the function select the "best" regression, that is one where all variables are significant, all specification tests are passed, and, if more than one regression has these properties, the one with the smallest aic criterion. The program stores results in a tlist (itself built with regression tlists) and prints the results along the options given by the user.
EXAMPLES
1) [r1]=automatic('del(lm1-lp)','lagts(lm1-lp-ly)','del(lp)','rnet','del(lagts(lm1-lp))', 'del(lagts(2,lm1-lp))','del(lagts(3,lm1-lp))','del(lagts(4,lm1-lp))','del(ly)','del(lagts(1,ly))', 'del(lagts(2,ly))',del(lagts(3,ly))','del(lagts(4,ly))','del(del(lp))','del(del(lagts(1,lp)))','del(del(lagts(2,lp)))','del(del(lagts(3,lp)))','del(del(lagts(4,lp)))','del(rnet)','del(lagts(1,rnet))','del(lagts(2,rnet))','del(lagts(3,rnet))','del(lagts(4,rnet))','cte','prt=initial,final','test=predfailin(0.5),predfailin(0.9),doornhans,arlm(5),hetero_sq')
2) [r2]=automatic('del(lm1-lp)','lagts(lm1-lp-ly)', 'del(lagts(lp))','lagts(rnet)','del(lagts(lm1-lp))','del(lagts(2,lm1-lp))','del(lagts(3,lm1-lp))','del(lagts(4,lm1-lp))','del(ly)','del(lagts(1,ly))', 'del(lagts(2,ly))','del(lagts(3,ly))','del(lagts(4,ly))','del(del(lp))','del(del(lagts(1,lp)))','del(del(lagts(2,lp)))','del(del(lagts(3,lp)))','del(del(lagts(4,lp)))','del(rnet)','del(lagts(1,rnet))','del(lagts(2,rnet))','del(lagts(3,rnet))','del(lagts(4,rnet))','cte','prt=final','test=predfailin(0.5),predfailin(0.9),doornhans,arlm(5),hetero_sq')
3) [r3]=automatic('lm1','lagts(lm1)','lagts(2,lm1)','lagts(3,lm1)','lagts(4,lm1)', 'lp','lagts(1,lp)','lagts(2,lp)','lagts(3,lp)','lagts(4,lp)','ly','lagts(1,ly)','lagts(2,ly)','lagts(3,ly)','lagts(4,ly)','rnet','lagts(1,rnet)','lagts(2,rnet)','lagts(3,rnet)','lagts(4,rnet)','cte','prt=initial,final,st2_mod','test=predfailin(0.51),predfailin(0.862),doornhans,arlm(5),hetero_sq','alpha=0.05')
All these examples start from a rather extensive set of exogenous variables: from 'lagts(lm1-lp-ly)' to 'cte' in examples 1 and 2 and from 'lagts(lm1)' in example 3.
AUTHOR
Eric Dubois 2002