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RE: st: Coefficient Constraints as Counterfactuals

From   Richard Williams <>
To, "" <>
Subject   RE: st: Coefficient Constraints as Counterfactuals
Date   Thu, 10 Jan 2013 15:22:39 -0500

Here is an example using -linest- (which, of course, requires that you install -linest-. It is from the STB and can be found with -findit-)

use "";, clear
logit warmlt3 yr89 male white age ed
test yr89 = -.5, coef
constraint 1 yr89 = -.5
linest, c(1) modify
logit warmlt3 yr89 male white age ed, constraint(1)

Note that the test command and linest produce the same coefficients. Further imposing constraints after estimation gives different results than imposing constraints before estimation -- but they are supposed to be asympotically equivalent (like the difference between a LR chi square and a Wald chi-square).

I don't know if it works with xtpcse, but you can try it.

At 02:18 PM 1/10/2013, Matthew C Mahutga wrote:
Hi Nick.

Thanks for this and for asking me to clarify.

I'm using xtpcse with a first order autocorrelation correction.

Do I understand you correctly that if my original model (with other covariates omitted) is

Y = b0 + b1x1+b2x2+b3x1*x2

And I want to estimate the predicted values of a model in which b1 = b1+b3, then I would regress Y^(=y-b1+b3x1) on x2 and the rest of the covariates and then estimated the prediction?

Thanks again,


-----Original Message-----
From: [] On Behalf Of Nick Cox
Sent: Thursday, January 10, 2013 10:41 AM
Subject: Re: st: Coefficient Constraints as Counterfactuals

If you want say to -regress- such that

y = b_0 + b_1 x_1 + 42 x_2

then calculate

y - 42 x_2

and -regress- on x_1. Naturally, this isn't universal, but what could be universal across all possible estimation commands? Moral: You should tell us more about the command you are using.


On Thu, Jan 10, 2013 at 6:25 PM, Matthew C Mahutga <> wrote:

> I have a question that I hope has an easy answer that escapes me.
> I am using estimation command that does not support the constraints option, but I would like to constrain a select group of coefficients. For example, one model includes an interaction term between a given socioeconomic process and a dummy variable for institutional regime. I would like to ask questions like "what would the trend in my outcome look like if the socioeconomic process had the larger of the two conditional (i.e. by regime type) effects".
> Is there a universal means by which to set a given coefficient to a fixed value prior to model estimation. Or, can I estimate predicted values using stored results but change the coefficient on one covariate beforehand?

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