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From |
Eric Sevigny <els21+@pitt.edu> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: Design-based truncated regression |

Date |
Thu, 07 Oct 2004 14:41:48 -0400 |

Hello Statalist,

My question involves Stata's capability to estimate design-based truncation regression models. I am using complex survey data, incorporating weights, strata, and psu. I also have a truncated dependent variable in which I have no observations for cases when y is below a certain cutoff and, therefore, want to estimate a truncated regression model. While Stata's survey estimation capabilities include models for other limited dependent variables (e.g., svyintreg), it does not explicitly implement this capability for truncated regression. Thus, as I see it, I have several options, which I would appreciate any comment on.

1) Use -truncreg- options to estimate truncated regression models that partially adjust for design elements. Specifically, that would mean ignoring strata (or disaggregating analyses by strata) while accounting for just the pweights and clusters. This is unsatisfactory because (i) possible problems in variance estimates for analyses that are not fully disaggregated by strata and (ii) problems with having incorrect df in any postestimation tests.

2) Estimate totally model-based -truncreg- and then use -suest- to survey adjust the estimates. Unfortunately, this won't work unless -truncreg- is somehow reprogrammed to accept iweights, which are needed for use with -suest-. Even if this capability were implemented, I am not sure postestimation tests would be performed with correct df.

3) This is probably the best option, but I'm not sure I have the statistical/programming ability to implement it. It is to use Stata's -ml- capabilities to create an estimation command that accounts for both the truncation and survey design. I know -ml- has survey estimation capabilities, but am not sure if truncated regression estimation has any inherent or theoretical limitations for such implementation.

In any event, I would greatly appreciate any technical, methodological, programming, and/or theoretical comments anyone could provide on this issue.

Thanks,

Eric Sevigny

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