----- Original Message -----
From: "Marina Balboa" <[email protected]>
To: <[email protected]>
Sent: Wednesday, July 23, 2003 11:37 AM
Subject: st: marginal effects
> Dear statalist,
>
> I tried to send the same mail but it does never arrive to the list. I hope
this one does.
>
> I solved my doubt this weekend, but it still remains one. I want to calculate
the marginal effects for the observed censored variable "y*" in xttobit (not the
latent variable, "y". In the manual "y*" also refers to the observed variable
and "y" to the latent, doesn't it?). I can work with the conditional or
unconditional expectation. The variable y* is censored at zero.
>
> To calculate marginal effects using E(y* |x, y*>0) (and assuming u_i=0), I
type
> "mfx compute, predict(ystar0(0,.))"
>
> To calculate E(y* |x) (u_i=0), I type
> "mfx compute, predict(ystar0(.,.))". However, this latter option gives me the
same coefficients as the ones in the result of xttobit, which are the ones for
the latent variable. Wouldn't be the coefficients I should obtain with this
command the result of multiplying the betas of the xttobit by the
normprob(x*beta)?
>
> Thanks a lot in advance for any hint you could give me. It will be much
welcomed.
> Sincerely,
> Marina Balboa
>
Marina,
mfx compute, predict(e(lower_bound, upper_bound)) gives the marginal effects for
the expected value of y conditional on being uncensored.
mfx compute, predict(ys(lower_bound, upper_bound) gives the marginal effects for
the unconditional expected value of y.
In both cases, you need to specify the left and / or right censoring point.
Example:
. use "C:\Stata8\auto.dta", clear
(1978 Automobile Data)
. replace price = 4000 if price <4000
(11 real changes made)
. xttobit price mpg, ll(4000) i(foreign) nolog
Random-effects tobit regression Number of obs = 74
Group variable (i): foreign Number of groups = 2
Random effects u_i ~ Gaussian Obs per group: min = 22
avg = 37.0
max = 52
Wald chi2(1) = 25.39
Log likelihood = -597.93768 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
price | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
mpg | -327.9141 65.0826 -5.04 0.000 -455.4736 -200.3545
_cons | 13099.53 1578.574 8.30 0.000 10005.58 16193.48
-------------+----------------------------------------------------------------
/sigma_u | 707.8464 527.0417 1.34 0.179 -325.1364 1740.829
/sigma_e | 2762.649 252.5473 10.94 0.000 2267.665 3257.632
-------------+----------------------------------------------------------------
rho | .0616045 .0871224 .0016116 .4454072
------------------------------------------------------------------------------
Observation summary: 63 uncensored observations
11 left-censored observations
0 right-censored observations
. mfx compute, nose
Marginal effects after xttobit
y = Fitted values (predict)
= 6115.8478
-------------------------------------------------------------------------------
variable | dy/dx X
---------------------------------+---------------------------------------------
mpg | -327.9141 21.2973
-------------------------------------------------------------------------------
. ****This is the latent value.
. mfx compute, nose predict(p(4000,.))
Marginal effects after xttobit
y = Pr(price>4000) (predict, p(4000,.))
= .77092933
-------------------------------------------------------------------------------
variable | dy/dx X
---------------------------------+---------------------------------------------
mpg | -.0348347 21.2973
-------------------------------------------------------------------------------
. ****This is the marginal effects for the probability of being uncensored
. mfx compute, nose predict(e(4000,.))
Marginal effects after xttobit
y = E(price|price>4000) (predict, e(4000,.))
= 7236.5842
-------------------------------------------------------------------------------
variable | dy/dx X
---------------------------------+---------------------------------------------
mpg | -181.6677 21.2973
-------------------------------------------------------------------------------
. ****The marginal effects for the expected value of y conditional on being
uncensored
. mfx compute, nose predict(ys(4000,.))
Marginal effects after xttobit
y = E(price*|price>4000) (predict, ys(4000,.))
= 6495.1777
-------------------------------------------------------------------------------
variable | dy/dx X
---------------------------------+---------------------------------------------
mpg | -252.7986 21.2973
-------------------------------------------------------------------------------
. ****The marginal effects for the unconditional expected value of y
.
Hope this helps,
Scott
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