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st: question with MLE and complex model
Greetings,
I am working on an MLE procedure to use in situations where there may
be distinct, or at least mostly distinct, causal processes at work for
different parts of the sample. For example, suppose there are three
different states of the world, and the coefficients on key explanatory
variables vary across these states. Additionally, suppose that there
is a set of variables that determines (probabilistically) the extent
to which to a particular case falls into each state.
In other words, I have three linear models: mod1, mod2, and mod3.
Also, I have a weighting function, similar to that used in multinomial
logit, that estimates a set of weights that sum to 1: p1 + p2 + p3.
I am fairly new to ML programming, so I started with the ML version of
a standard linear regression (with adjustments to s_e suggested by
Gould and Sribney) and incorporated the three linear models with their
corresponding weighting functions. The trouble is, when I try to
estimate the model, Stata goes through thousands of iterations, nearly
all of which report "not concave". Convergence is achieved only
rarely, and it depends very much upon specification.
program define stage3ml
args lnf mod1 st1 mod2 st2 mod3 ctrls s_e
tempvar den p1 p2 p3
quietly gen double `den' = 1 + exp(`st1') + exp(`st2')
quietly gen double `p1' = exp(`st1')/`den'
quietly gen double `p2' = exp(`st2')/`den'
quietly gen double `p3' = 1/`den'
quietly replace `lnf'=ln(normalden(($ML_y1 - `p1'*`mod1' -
`p2'*`mod2' - `p3'*`mod3' - `ctrls')/exp(`s_e'))) - `s_e'
end
ml model lf stage3ml (mod1: y = x1 x2 x3) (p1: z1 z2 z3) (mod2: x1 x2
x3) (p2: z1 z2 z3) (mod3: x1 x2 x3) (ctrs: ) ()
Any advice on what steps I should next take would be greatly
appreciated!
Many thanks,
Jonathan Hanson
Assistant Professor of Political Science
Maxwell School of Citizenship and Public Affairs
100 Eggers Hall
Syracuse University
Syracuse, NY 13244
[email protected]
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