Estimate non-linear least squares for equations of a model
[model2,rnls]=nlsmod(model,tsmat,indeq,arg1,...,argn)
* model = a model tlist
* tsmat = a tsmat containing all data needed for estimating the equation (should be the tsmat associated to the model, created by function create_dbmod)
* indeq =
- a string, the name of the equation to estimate or the keyword 'all' (to estimate all equations)
- or an interger, the # of the equation in the model
* arg1,...,argn = optional arguments
- 'noprint' if the user does not want to print the result (defautlt: results are displayed on screen)
- 'save=%t' if the user wants to save the estimated coefficients in the model tlist
- 'optfunc=optim' if the user wants to use the optim optimisation function (default: optimg)
- 'opt_nelmead=crit,nitermax' with crit the value of the convergence criterion in the Nelder-Meade optimisation function and nitermax the maximum number of iterations (default = 'opt_nelmead=2*%eps,1000')
- 'opt_optim=opts' where opts are options for optim that can be entered after the starting value of the parameters (default = 'opt_optim=,''ar'',1e6,1e6'')
- 'opt_convg=val' where val is the threshold on gradient norm (default = 'opt_convg=1e-5')
* model2 = the model tlist, with the estimated coefficients if the option save has been swtiched to %t
* rnls = a nls results tlist, collecting the results of the corresponding estimated equation
global GROCERDIR // load the model small: load(GROCERDIR+'data\small.dat') // load the database small_db: load(GROCERDIR+'data\small_db.dat') // set the bounds: bounds('1981q1','2005q4'); // In the small model, estimate by non-linear least squares the equation td_p6_d1 // Estimated coefficients are stored into the small model tlist and // the whole estimation results are stored into tlist rp6_d1: [small,rp6_d1]=nlsmod(small,small_db,'td_p6_d1','save=%t') |  |  |