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From |
Kit Baum <baum@bc.edu> |

To |
"Solomon Tesfu" <ecosttx@langate.gsu.edu> |

Subject |
Re: st: Re: Adding the marginal effects at individual values of |

Date |
Mon, 22 Feb 2010 20:53:28 -0500 |

<> Short answer: yes. set more off clear all use "http://www.indiana.edu/~jslsoc/stata/spex_data/ordwarm2.dta";, clear logit warmlt2 age ed prst, nolog // for a continuous variable that takes on discrete values, // evaluate the average marginal effect at each such value margins, dydx(ed) at(ed=(0/20)) mat ameed = r(b)' mat li ameed g ed2 = ed*10 logit warmlt2 age ed2 prst, nolog margins, dydx(ed2) at(ed=(0(10)200)) mat ameed2 = 10 * r(b)' mat li ameed2 mat diff = (ameed - ameed2) mat li diff Remember that the probit model is linear in the latent (index) variable I = X b. The nonlinearity arises when you evaluate the Normal CDF. Just as in regression, multiplying X by k gives you b* = b / k. On Feb 22, 2010, at 8:26 PM, Solomon Tesfu wrote: > I'm sorry about this but I'm not sure why the MEs calculated at -60, -59, -58,...,58, 59, 60 should be related to those at -6.0, -5.9, -5.8,..., 5.8, 5.9, 6.0 in a heavily non-linear model like probit . Are you suggesting that they are equivalent or proportional or...? > > Thanks again, > > Solomon > >>>> Christopher Baum <baum@bc.edu> 02/22/10 1:33 PM >>> > As I said in an earlier message, you can do this on a fine grid. > Multiply the variable of interest by 10 and it will range from -60 to > +60, and you can step through those 120 integers and calculate AMEs > for each of them, corresponding to the original variable evaluated at > -6.0, -5.9, -5.8, ... > > I suggest making integer-valued 'buckets' out of this to make exact > comparisons hassle-free. > > Kit Baum | Boston College Economics and DIW Berlin | http://ideas.repec.org/e/pba1.html > An Introduction to Stata Programming | http://www.stata-press.com/books/isp.html > An Introduction to Modern Econometrics Using Stata | http://www.stata-press.com/books/imeus.html > > On Feb 22, 2010, at 1:09 PM, Solomon Tesfu wrote: > >> Thanks again for your helpful suggestions . When I said the AME does >> not show the variations in the ME at various levels of the regressor >> I was refering to the AME calculated using the entire set of >> observations. Yes, I can see the pattern in the AME by calculating >> it for successively increasing intervals of the observed values of >> the regressor. But my undertanding of the syntax you suggested was >> that it calculates the MEs at only integer points (not the AMEs for >> intervals of values) and adds them to the data as an additional >> variable. The observed values of my variable of interest range >> between -6 and 6 and the sample size is 2400. If I round off all the >> observed values to the nearest integers and calculate the MEs only >> at integer points that will still be informative but will hide some >> details. Anyway, I think I have sufficient inputs from you guys and >> I'll work on it. >> >> Solomon >> >>>>> Kit Baum <baum@bc.edu> 02/22/10 7:27 AM >>> >> On Feb 22, 2010, at 2:33 AM, Solomon wrote: >> >>> Thanks again Kit and Richard, for your ideas. I understand that I >>> cannot talk about precision of the estimates at each point of >>> observation but once I get the estimates I can plot them against >>> the values of the variable and look at the pattern. This is >>> important because I have a reason to believe that the marginal >>> effects will be different at high and low values of the regressor >>> and the AME or the marginal effect at mean do not help me to verify >>> this possibility. >> >> I don't see, then, how calculating AMEs at various points in the >> regressor space would not 'verift this possibility'. If you take the >> continuous variable you have and 'bin' it into ranges---which can be >> as many as you can handle, given matsize---you can calculate the >> AMEs at very-very-low, very-low, low, low+, low++, low+++, etc. >> values of that regressor. Depending on your sample size and the >> capacity of Stata (e.g., Stata/SE or Stata/MP can handle larger >> matrices) you could calculate AMEs on a very fine grid of values of >> the regressor, and 'look at the pattern'. Why does this not answer >> the question you'd like to pose to the data? >> >> If AMEs differ across levels of income, I don't need to use an >> income of $54,321 to verify that. An income of $55,000 would work, >> as long as its AME is clearly distinct from that of income = $5,000. >> >> Kit Baum | Boston College Economics & DIW Berlin | http://ideas.repec.org/e/pba1.html >> An Introduction to Stata Programming >> | http://www.stata-press.com/books/isp.html >> An Introduction to Modern Econometrics Using Stata | http://www.stata-press.com/books/imeus.html >> >> >> * >> * For searches and help try: >> * http://www.stata.com/help.cgi?search >> * http://www.stata.com/support/statalist/faq >> * http://www.ats.ucla.edu/stat/stata/ >> > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > Kit Baum | Boston College Economics & DIW Berlin | http://ideas.repec.org/e/pba1.html An Introduction to Stata Programming | http://www.stata-press.com/books/isp.html An Introduction to Modern Econometrics Using Stata | http://www.stata-press.com/books/imeus.html * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**Re: st: Re: Adding the marginal effects at individual values of***From:*"Solomon Tesfu" <ecosttx@langate.gsu.edu>

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