🚀 go-pugleaf

Hello all:

	I am estimating the parameters of a censored regression model
(panel tobit), and one of the variables has a non-linear effect on
the dependent variable. I am trying to use a spline to model this, 
and its defined as:

	V1 = THETA + MIN(DIFF-THETA,0)
	V2 = MAX(DIFF-THETA,0)

so that if DIFF is less than THETA, then the effect on the dependent
variable is different than when it is larger, and V1 and V2 are the
two secondary independent variables created from the DIFF variable.
Instead  of imposing a value on THETA, I'd like to be able to estimate
it from the data itself. 

My questions are:

(1) OPTIMIZATION STRATEGY: ML estimation will require some type of
non-gradient  technique (?), so how do I get the asymtotic t-stats and
such? 

(2) MEANING OF T-STATS: If I am estimating the join point from the
data itself, does that require some special tweaking on the t-ratios
to get legitimate tests of significance?


(3) REFERENCES: Can anyone suggest references that I can explore on
this problem?

(4) ALTERNATIVES: Can anyone suggest alternatives that would allow for
similar effects, other than introducing a polynomial in DIFF, of which
I'm already aware of.

Thanks for any assistance.


Seasons Greetings!

Gyan P. Sinha
UBC, Vancouver.