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From |
Ed Blackburne <blackburne@shsu.edu> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Defining variables for the ml procedure |

Date |
Mon, 10 Oct 2005 18:42:08 -0500 |

Bjorn, I do have a solution in mind that will do what you want. When I get near a Stata machine, I will write a bit of code to show you. I struggled with a problem close to what you're having, so I am glad to help out if, in fact, it saves you some time. If you are able to send me a brief message, perhaps offlist, detailing the exact nature of the model you wish to estimate, I can get the code as close as possible. For example, it appears you have a panel dataset -- are you estimating pooled coefficients (common slopes), heterogeneous panels, a combination, or what? My email address is blackburne at shsu dot edu -Ed On Mon, 2005-10-10 at 17:02 -0400, bpersson@bu.edu wrote: > Ed, > > Thank you very much for your message, your code does accomplish what I > want to do in principle. > > Unfortunately, however, the regression equation I want to estimate is > actually in logarithms: > > log(consumption) = a + b*(log(c*income[_n-1] + invest)) > > (I am sorry, I should have mentioned this). In this case, I guess I > cannot use this method since it is no longer possible to retrieve the > parameter c, right? Would you have any ideas on an alternative method? > > Thanks again for all your help! > > Best Regards > Bjorn > > > > > Quoting Ed Blackburne <blackburne@shsu.edu>: > > > Bjorn, > > > > I am perhaps misunderstanding your model. > > > > Here is a code snippet you can paste into to Stata and run. > > > > ****************Begin do file******************** > > > > webuse lutkepohl > > > > *** Estimate consumption=a+b*(c*income[_n-1]+invest) > > *** The reduced form is consumption=a + d*l.income + b*investment > > *** This is what we will estimate with Stata > > > > regress consu l.income invest > > > > *** From these estimates, we can recover parameter c. > > *** Since d=b*c, we have c=d/b > > *** In Stata speak this is _b[l.income]/_b[investment] > > > > nlcom (_b[l.income]/_b[investment]) > > > > *************End do file > > > > > > Does that accomplish your task? > > > > Let me know. > > > > Ed > > > > > > On Mon, 2005-10-10 at 13:47 -0400, bpersson@bu.edu wrote: > >> Hi Ed, > >> > >> Thank you for your reply. Your Option 2 sounds very promising and I > >> would be very grateful for any hints and advice you might have on how > >> to use it. > >> > >> (As I understand it, I cannot use Option 1 because the variable Z is > >> unobserved. Since it depends on the unknown coefficient c, the > >> variable Z must somehow be generated as a part of (that is, > >> simultaneously as) the estimation of the regression equation > >> > >> y = a + b*(c*Z[_n-1] + x)) > >> > >> Please let me know if this is not clear, and thanks for your help! > >> > >> Best Regards > >> Bjorn > >> > >> > >> > >> Quoting Ed Blackburne <blackburne@shsu.edu>: > >> > >> > Bjorn, > >> > > >> > My understanding is that you want to estimate an equation of the form > >> > (it doesn't much matter whether it is Z or Z[_N-1] for this post): > >> > > >> > y=a+b(cZ+x) > >> > > >> > Is this correct? > >> > If so, I see two options. > >> > > >> > Option 1: Estimate y=a+dZ+bx and use nlcom to recover c: > >> > nlcom _b[Z]/_b[x] > >> > > >> > Option 2: You can estimate via conditional likelihood (aka profile > >> > likelihoods) using the undocumented hold option of ml. > >> > > >> > If you would like a pointer at Option 2 let me know. > >> > > >> > -Ed > >> > > >> > > >> > On Sun, 2005-10-09 at 12:59 -0400, bpersson@bu.edu wrote: > >> >> Dear Statalist, > >> >> > >> >> I am trying to estimate a nonlinear regression equation using the ml > >> >> routine, and I am having problems defining one of my input variables. > >> >> Specifically, I would like to use the recursively defined variable > >> >> Z(it): > >> >> > >> >> Z(it) = c*Z(it-1) + X(it) > >> >> > >> >> as an independent variable in my regression equation. If the > >> >> coefficient "c" is known, this variable can be generated as follows: > >> >> > >> >> gen Z = X > >> >> bysort group (time): replace Z = c * Z[_n-1] + X if _n >= 2 > >> >> > >> >> My problem is that I would like to estimate the coefficient "c" (along > >> >> with the other regression coefficients) and therefore I need to define > >> >> the variable Z(it) "within" the program itself. > >> >> > >> >> It seems, however, as if the "by" command and the "if" command are not > >> >> allowed when specifying variables in the ml programs. I would be most > >> >> grateful if anyone would have any ideas on how to do this. > >> >> > >> >> Thank you for your help. > >> >> > >> >> Best > >> >> Bjorn > >> >> > >> >> > >> >> > >> >> * > >> >> * For searches and help try: > >> >> * http://www.stata.com/support/faqs/res/findit.html > >> >> * http://www.stata.com/support/statalist/faq > >> >> * http://www.ats.ucla.edu/stat/stata/ > >> > > >> > * > >> > * For searches and help try: > >> > * http://www.stata.com/support/faqs/res/findit.html > >> > * http://www.stata.com/support/statalist/faq > >> > * http://www.ats.ucla.edu/stat/stata/ > >> > >> > >> > >> > >> > >> * > >> * For searches and help try: > >> * http://www.stata.com/support/faqs/res/findit.html > >> * http://www.stata.com/support/statalist/faq > >> * http://www.ats.ucla.edu/stat/stata/ > > > > * > > * For searches and help try: > > * http://www.stata.com/support/faqs/res/findit.html > > * http://www.stata.com/support/statalist/faq > > * http://www.ats.ucla.edu/stat/stata/ > > > > > > * > * For searches and help try: > * http://www.stata.com/support/faqs/res/findit.html > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Defining variables for the ml procedure***From:*bpersson@bu.edu

**Re: st: Defining variables for the ml procedure***From:*Ed Blackburne <blackburne@shsu.edu>

**Re: st: Defining variables for the ml procedure***From:*bpersson@bu.edu

**Re: st: Defining variables for the ml procedure***From:*Ed Blackburne <blackburne@shsu.edu>

**Re: st: Defining variables for the ml procedure***From:*bpersson@bu.edu

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