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From |
Austin Nichols <austinnichols@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: GMM minimization of regional errors imputed from hhd level model |

Date |
Sat, 29 Jun 2013 09:27:40 -0400 |

Vladimír Hlásny <vhlasny@gmail.com>: I have not read the ref. But you do not really have instruments. That is, you are not setting E(Ze) to zero with e a residual from some equation and Z your instrument; you do not have moments of that type. Seems you should start with optimize() instead of -gmm-, as you are just minimizing the sum of squared deviations from targets at the region level. Or am I still misunderstanding this exercise? On Fri, Jun 28, 2013 at 10:08 PM, Vladimír Hlásny <vhlasny@gmail.com> wrote: > Thanks for responding, Austin. > > The full reference is: Korinek, Mistiaen and Ravallion (2007), An > econometric method of correcting for unit nonresponse bias in surveys, > J. of Econometrics 136. > > My sample includes 12000 responding households. I know their income, > and which of 2500 regions they come from. In addition, for each > region, I know the number of non-responding households. I find the > coefficient on income by fitting estimated regional population to > actual population: > > P_i = logit f(income_i,theta) > actual_j = responding_j + nonresponding_j > theta = argmin {sum(1/P_i) - actual_j} > > Response probability may not be monotonic in income. The logit may be > a non-monotonic function of income. > > Thanks for any thoughts on how to estimate this in Stata, or how to > make my 'trick' (setting 12000-2500 hhd-level residuals manually to > zero) work better. > > Vladimir > > On Sat, Jun 29, 2013 at 1:49 AM, Austin Nichols <austinnichols@gmail.com> wrote: >> Vladimír Hlásny <vhlasny@gmail.com>: >> As the FAQ hints, if you don't provide full references, don't expect >> good answers. >> >> I don't understand your description--how are you running a logit of >> response on income, when you only have income for responders? Can you >> give a sense of what the data looks like? >> >> On another topic, why would anyone expect response probability to be >> monotonic in income? >> >> On Fri, Jun 28, 2013 at 10:05 AM, Vladimír Hlásny <vhlasny@gmail.com> wrote: >>> Hi, >>> I am using a method by Korinek, Mistiaen and Ravallion (2007) to >>> correct for unit-nonresponse bias. That involves estimating >>> response-probability for each household, inferring regional >>> population from these probabilities, and fitting against actual >>> regional populations. I must use household-level data and region-level >>> data simultaneously, because coefficients in the household-level model >>> are adjusted based on fit of the regional-level populations. >>> >>> I used a trick - manually resetting residuals of all but >>> one-per-region household - but this trick doesn't produce perfect >>> results. Please find the details, remaining problems, as well as the >>> Stata code described below. Any thoughts on this? >>> >>> Thank you for any suggestions! >>> >>> Vladimir Hlasny >>> Ewha Womans University >>> Seoul, Korea >>> >>> Details: >>> I am estimating households' probability to respond to a survey as a >>> function of their income. For each responding household (12000), I >>> have data on income. Also, at the level of region (3000), I know the >>> number of responding and non-responding households. >>> >>> I declare a logit equation of response-probability as a function of >>> income, to estimate it for all responding households. >>> >>> The identification is provided by fitting of population in each >>> region. For each responding household, I estimate their true mass as >>> the inverse of their response probability. Then I sum the >>> response-probabilities for all households in a region, and fit it >>> against the true population. >>> >>> Stata problem: >>> I am estimating GMM at the regional level. But, to obtain the >>> population estimate in each region, I calculate response-probabilities >>> at the household level and sum them up in a region. This region-level >>> fitting and response-probability estimation occurs >>> simultaneously/iteratively -- as logit-coefficients are adjusted to >>> minimize region-level residuals, households response-probabilities >>> change. * * 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: GMM minimization of regional errors imputed from hhd level model***From:*Vladimír Hlásny <vhlasny@gmail.com>

**References**:**st: GMM minimization of regional errors imputed from hhd level model***From:*Vladimír Hlásny <vhlasny@gmail.com>

**Re: st: GMM minimization of regional errors imputed from hhd level model***From:*Austin Nichols <austinnichols@gmail.com>

**Re: st: GMM minimization of regional errors imputed from hhd level model***From:*Vladimír Hlásny <vhlasny@gmail.com>

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