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From |
Suryadipta Roy <sroy2138@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: Interpretation of interaction term in log linear (non linear) model |

Date |
Sat, 8 Jun 2013 12:12:04 -0400 |

Dear Statalisters, I was wondering if some one would be kind enough to clarify if I am on the right track in clarifying the coefficient of the interaction term when the dependent variable is in logarithm. The estimated model is of the form: log(Trade) = constant + 0.15dummy - 0.15x1 + 0.12dummy*x1, where dummy is (0,1) categorical variable, x1 is a continuous variable (standardized 0 - 1), and dummy*x1 is the interaction term. The result has been obtained from a fixed effects panel regression using -areg- with robust standard error option, and all the variables are statistically significant. Based on readings of Maarten's Stata tip 87: Interpretation of interactions in non-linear model, several Statalist postings, and the following link http://www.stanford.edu/~mrosenfe/soc_388_notes/soc_388_2002/Interpreting%20the%20coefficients%20of%20loglinear%20models.pdf , I wanted to make sure if any of the following interpretation of the above result is correct: 1. The coefficient of "dummy" indicates that this category (dummy variable = 1) has 16% (= exp(0.15) - 1) more of "Trade" compared to the base category (dummy variable = 0). 2. The effect of being in this category on "Trade" increases when the value of x1 increases. For every standard deviation increase in x1, the effect of "dummy" increases by about 13% (exp(0.12) - 1), OR there is a statistically significant 13% increase in "Trade" to countries having more of x1 relative to countries that have one standard deviation lower value of x1, OR the effect of being in the "dummy = 1" category in a country with one standard deviation more of x1 than average is exp(0.12)*exp(0.15) = 1.31, which means that "dummy=1" category has about 31% more "Trade" than "dummy=0" category. Following suggestions elsewhere in the Statalist, I have pursued other non-linear estimation strategies (and have asked questions to that effect earlier), but there is a tradition in this literature to use log-linear models. Any suggestion is greatly appreciated. Sincerely, Suryadipta Roy. * * 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: Interpretation of interaction term in log linear (non linear) model***From:*David Hoaglin <dchoaglin@gmail.com>

**Re: st: Interpretation of interaction term in log linear (non linear) model***From:*"Dimitriy V. Masterov" <dvmaster@gmail.com>

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