Notice: On March 31, it was **announced** that Statalist is moving from an email list to a **forum**. The old list will shut down on April 23, and its replacement, **statalist.org** is already up and running.

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

From |
Lorenzo Ciari <lorenzo.ciari@learlab.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: FE: st: Interaction effects |

Date |
Mon, 14 Jun 2010 21:33:53 +0200 |

Thank you very much.

Il 14/06/2010 21.17, frone@ria.buffalo.edu ha scritto:

--- On Mon, 14/6/10, Lorenzo Ciari wrote:I am estimating a probit model and my dependent variable is entry (0-1) in a given market. I estimate the model using form level data, so for each firm I observe whether it enters or not a market (I have multiple markets): I want to test whether entry depends on a given variable (call it COMP) and see whether the effect of COMP on entry is particularly strong for firsm with certain characteristics (suppose the characteristics SIZE). 1) Question 1: if I want to test the hipothesis that COMP has no effect for values of SIZE lower that X, can I create a dummy = 1 for firms with size<X and then estimate a model with COMP, SIZE (continuous variable) and the interaction between (COMP) and the dummy?No. The variable used to represent SIZE in the cross-product needs to be the same as that used for the lower order terms. The cross-product is not an interaction until the constituent variables are partialled out. So the lower order SIZE should be the same as that used to compute the cross-product. So you could estimate: COMP, SIZE (continuous variable) and the interaction between COMP and SIZE (continuous) or COMP, SIZE (dummy) and the interaction between COMP and SIZE (dummy) But if you have a continuous measure of SIZE, the first approach is preferable.Can I interpret the interaction coefficient as in OLS (I wouldn't know how to use AI-Norton inteff command within this framework, as I do not interact SIZE with COMP, but the new created dummy....Greene addresses the suggestion by Ai& Norton and, in contrast to Ai& Norton, would suggest yes. Greene argues that hypothesis tests (about interactions) "are about model coefficients and about the structural aspects of the model specifications. Partial effects are neither coefficients nor elements of the specification of the model. They are implications of the specified and estimated model." Then the interaction can be graphically portrayed in a variety of ways, including predicted probabilities. A problem I've had with the Ai& Norton approach is that they fail to demonstrate how their plots have any useful substantive interpretation regarding the "form" of an interaction between predictors. Understanding the form (shape) of the interaction is much more important than merely knowing whether or not it is significantly different from zero. This is also discussed by Greene--see: http://w4.stern.nyu.edu/economics/docs/workingpapers/2009/Interaction-Terms-in-Nonlinear-Models.pdf Also take a look at: http://ideas.repec.org/p/iza/izadps/dp3478.html Mike Frone **************************************************************** Michael R. Frone, Ph.D. Senior Research Scientist Research Institute on Addictions State University of New York at Buffalo 1021 Main Street Buffalo, New York 14203 Office: 716-887-2519 Fax: 716-887-2477 E-mail: frone@ria.buffalo.edu Internet: http://www.ria.buffalo.edu/profiles/frone.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/

**References**:**FE: st: Interaction effects***From:*frone@ria.buffalo.edu

- Prev by Date:
**st: clogit with cluster-level variables** - Next by Date:
**st: clogit with cluster-level variables** - Previous by thread:
**FE: st: Interaction effects** - Next by thread:
**Re: FE: st: Interaction effects** - Index(es):