Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
Maarten Buis <maartenlbuis@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: perform tests with marginal effects |

Date |
Tue, 22 Jan 2013 10:50:55 +0100 |

On Mon, Jan 21, 2013 at 7:03 PM, Andreas Fagereng wrote: > I run a probit of a dummy variable on the LHS and two dummy variables > on the RHS. > To get the marginal effects I use the mfx. > I then want to test wether the marginal effect of landsdel1 = e.g. 0.4 > When I do this however stata seems to take the coefficients form the > probit regression and not the marginal effects. > > How do I make the marginal effects testable? You'll need to use the -margins- command and estimate your model with the factor variable notations. See -help margins- and -help fvvarlist-. Below is an example. *------------------ begin example ------------------ sysuse nlsw88, clear gen byte marst = !never_married + married /// if !missing(never_married, married) label variable marst "marital status" label define marst 0 "never married" /// 1 "divorced or widowed" /// 2 "married" label value marst marst probit union i.marst margins , dydx(*) post test 2.marst = -.05 *------------------- end example ------------------- * (For more on examples I sent to the Statalist see: * http://www.maartenbuis.nl/example_faq ) In general, if all you are going to do is report and test one marginal marginal effect per parameter, either the average marginal effect (-margins-) or the marginal effect evaluated at the average of the explanatory variables (-mfx-), than you are better of using a linear probability model. In essence, such marginal effects estimate a linear model of your non-linear model of your data. If you are only interested in the results of your linear model, than it is better to cut out the middle man, and directly estimate a linear model of your data. It means less modeling, so less opportunity for making errors, and it is more honest about what your model and its limitations. Notice that I am not saying that a linear probability model is a good model, all I am saying is that it is a better model than estimating a non-linear (e.g. probit) model and than only interpret one marginal effect per variable. Personally, I prefer the logit model and interpret the results as odds ratios, as some people on this list know by now. However, there are situations where it does not matter which model you choose, as they will lead to exactly the same predictions. For example if your two indicator variables (I prefer the term indicator variable over dummy variable) are mutually exclusive, like the two indicator variables for -marst- in the example below, than a linear probability model, a risk-ratio model (-poisson-), a -logit-, and a -probit- model will result in exactly the same predictions. In this special situation, there is thus no reason to prefer one over the other, and you can just safely choose whichever model directly gives you the parameters you are looking for. *------------------ begin example ------------------ sysuse nlsw88, clear gen byte marst = !never_married + married /// if !missing(never_married, married) label variable marst "marital status" label define marst 0 "never married" /// 1 "divorced or widowed" /// 2 "married" label value marst marst // linear probabiltiy model reg union i.marst, vce(robust) predict pr_lpm // The constant means: never married has a 31% // chance of being a union member // 1.marst means: divorced have 4% points less // chance of being a union member // 2.marst means: married have a 8% points less // chance of being a union member // risk ratio model poisson union i.marst, vce(robust) irr predict pr_rr // The constant means: never married has a 31% // chance of being a union member // 1.marst means: the chance of being a union // member is 14% less when divorced (.86-1)*100%=-14% // 2.marst means: the chance of being a union // member is 25% less when married // Notice the difference between % point changes // and % changes. If we start with a baseline // value of 1% and change by 1 & point, then the // result will be 1 + 1 = 2%. If we change the // baseline value by 1%, the result will be // 1 * 1.01 = 1.01%. //logit model logit union i.marst, or predict pr_logit // The constant means: there are .44 union members // for every non-union member among never married // 1.marst means: this odds of being a union member // is 19% less when divorced // 2.marst means: this odds of being a union member // is 33% less when married //probit model probit union i.marst predict pr_probit // you could try to interpret the probit coefficients // in terms of a latent propensity of being a union // member, but I have never seen an application where // this type of interpretation convinced me // In this case all these models lead to exactly the // same predicted probiblities tab pr_lpm pr_probit tab pr_rr pr_probit tab pr_logit pr_probit *------------------- end example ------------------- * (For more on examples I sent to the Statalist see: * http://www.maartenbuis.nl/example_faq ) Hope this helps, Maarten --------------------------------- Maarten L. Buis WZB Reichpietschufer 50 10785 Berlin Germany http://www.maartenbuis.nl --------------------------------- * * 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/

**References**:**st: perform tests with marginal effects***From:*Andreas Fagereng <afagereng@gmail.com>

- Prev by Date:
**Re: st: Cut-off point for ROC curve using parametric and non-parametric method** - Next by Date:
**st: From: Maarten Buis <maartenlbuis@gmail.com>** - Previous by thread:
**st: perform tests with marginal effects** - Next by thread:
**st: Quai Maximum likelihood and multipul imputation** - Index(es):