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From |
Nick Cox <njcoxstata@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Coefficient Constraints as Counterfactuals |

Date |
Fri, 11 Jan 2013 00:05:56 +0000 |

Suppose your model is y = b_0 + b_1 x_1 + b_2 x_2 but for some reason you consider b_2 known (say 42). Then y = b_0 + b_1 x_1 + 42 x_2 Then it is also true that y - 42 x_2 = b_0 + b_1 x_1 and we form y - 42 x_2 = y* (say) and regress y* on x_1 to get estimates of b_0 and b_1. Clearly there is an assumption in there about zero average errors. y - 42 would make no sense if only on dimensional grounds. (It seems common that people in many fields of statistical science don't use dimensional thinking: see http://www.stata.com/statalist/archive/2009-10/msg00811.html for a recommendation of a paper by David Finney (1917- ).) Nick On Thu, Jan 10, 2013 at 10:06 PM, Matthew C Mahutga <matthew.mahutga@ucr.edu> wrote: > Thanks William and Nick. > > -linest- doesn't work for xtpcse, but I'm not sure why. It yields predicted values way beyond the observed bounds of the response. > > It works great for regress and xtreg (at least in my application) and is very intuitive to use. This does seem to be a nice approach insofar as it accommodates the covariance between parameters. But in my case, I'm engaging in pure unadulterated hypotheticals, and would actually prefer to keep the observed parameters fixed ;o). > > I attempted -estadd- or -eret2- to try to impose constraints post-facto, but had to admit they are beyond me. > > The procedure outlined by Nick gives intuitive results (at least for my purposes), but I'm still not exactly clear if I'm supposed to subtract from y (response, outcome, dependent) the constrained coefficient, or rather the product of the constrained coefficient and its covariate. My reading of his initial email leads me to believe it's the latter. > > Thanks for your help! > > Matthew > > -----Original Message----- > From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Richard Williams > Sent: Thursday, January 10, 2013 12:23 PM > To: statalist@hsphsun2.harvard.edu; statalist@hsphsun2.harvard.edu > Subject: RE: st: Coefficient Constraints as Counterfactuals > > 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 "http://www.indiana.edu/~jslsoc/stata/spex_data/ordwarm2.dta";, 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, >> >>Matthew >> >>-----Original Message----- >>From: owner-statalist@hsphsun2.harvard.edu >>[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Nick Cox >>Sent: Thursday, January 10, 2013 10:41 AM >>To: statalist@hsphsun2.harvard.edu >>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. >> >>Nick >> >>On Thu, Jan 10, 2013 at 6:25 PM, Matthew C Mahutga >><matthew.mahutga@ucr.edu> 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? * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**RE: st: Coefficient Constraints as Counterfactuals***From:*Matthew C Mahutga <matthew.mahutga@ucr.edu>

**References**:**st: Coefficient Constraints as Counterfactuals***From:*Matthew C Mahutga <matthew.mahutga@ucr.edu>

**Re: st: Coefficient Constraints as Counterfactuals***From:*Nick Cox <njcoxstata@gmail.com>

**RE: st: Coefficient Constraints as Counterfactuals***From:*Matthew C Mahutga <matthew.mahutga@ucr.edu>

**RE: st: Coefficient Constraints as Counterfactuals***From:*Richard Williams <richardwilliams.ndu@gmail.com>

**RE: st: Coefficient Constraints as Counterfactuals***From:*Matthew C Mahutga <matthew.mahutga@ucr.edu>

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