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From |
"John Reynolds" <[email protected]> |

To |
<[email protected]> |

Subject |
st: Interactions & Long's PRCHANGE (pred probs from LDVs) |

Date |
Tue, 9 Mar 2004 11:52:08 -0500 |

Looking for help w/ Long & Freese's PRCHANGE, part of their SPOST collection of ado files to help interpret limited dependent variable models. Question: How can I generate accurate predicted probabilities w/ PRCHANGE if my covariate of interest is "involved" in an interaction term? Details: I'm estimating ordered probit models that include an interaction term between a dummy variable and interval variable. For simplicitly, assume the following model, Y = b0 + b1*dummy + b2*interval + b3*dummy*interval. Long & Freese's PRCHANGE generates predicted probabilities and changes in probability holding all other variables constant at their sample means or at a pre-specified value. I want predicted probabilities for the two groups represented by the dummy variable, holding the interval variable constant at its sample mean. E.g., oprobit y dummy intval intxn prchange, x(dummy=1) would generate the predicted probabilities that y = k for cases which equal 1 on the dummy variable, holding the interval variable and the interaction term constant at their sample means. The problem is that the sample mean of "intval" would be the mean of the interval for all cases in the sample, while the sample mean of the interaction term "intxn" would equal the sum of the interval variable for dummy = 1 only, divided by the sample size. Instead, if I were to calculate the predicted probabilities by hand, I would use 1*(sample mean of interval variable) corresponding to dummy*interval. The issue is equally problematic when turning to the reference group on the dummy variable, i.e., oprobit y dummy intval intxn prchange, x(dummy=0). This model would estimate predicted probabilities and would again include a contribution of the interaction, even though the interaction term should drop out since it equals 0 for all dummy=0. Summary: Should I specify that PRCHANGE use group-specific means for the interaction terms, and overal sample means for the rest? Can I specify that with PRCHANGE? Is there a simpler solution I'm overlooking? For those of you how have a copy of Long & Freese's Stata book, could you verify for me whether this is addressed in section "8.2.1 Computing gender differences in predictions with interactions?" I plan to use this book next fall for class, but haven't bought it yet. Thanks for any assistance. John Reynolds, PhD Department of Sociology Florida State University Tallahassee, FL 32306-2270 850-644-4321 850-644-2304 (fax) email: [email protected] * * 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/

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