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From |
max.ch.h.bode@gmail.com |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: partialling out exogenous singleton fixed effects in iv-model (ivreg, ivregress) |

Date |
Fri, 29 Mar 2013 22:37:59 -0400 |

Hi statlisters, Some background: I am currently working on an IV model. The model is estimating the effect of access to microfinance (instrumented by distance to provider) on various outcome variables of interest (income etc.). So for the sack of an example suppose we have * 1st stage: take-up = b1 + b2*distancetoprovider + i.fe + e * 2nd stage: income = a1 + a2*predicted take-up + i.fe + e whereas i.fe are my raster fixed effects. I included raster fixed effects (whereas rasters are x^2 meters areas) into the model in order to ensure the exogeneity of the instrument. It is unavoidable that with the raster sizes I would like to use (in order to minimize endogeneity) I'll get a few singleton dummies, i.e., a variable with one 1 and N-1 zeros. Problem #1: So originally I used the -ivreg- command (as it easily tied into a program I wrote that produced stacked LaTeX tables). This command gave me an error message because I included singleton dummies (through the fixed effects) while requesting a robust covariance matrix. The error message further said I should try the -partial- option which I then did. Here the message: Warning: estimated covariance matrix of moment conditions not of full rank. standard errors and model tests should be interpreted with caution. Possible causes: singleton dummy variable (dummy with one 1 and N-1 0s or vice versa) partial option may address problem Here is a description of what the partial option is: The partial(varlist) option requests that the exogenous regressors in varlist are "partialled out" from all the other variables (other regressors and excluded instruments) in the estimation. If the equation includes a constant, it is also automatically partialled out as well. The coefficients corresponding to the regressors in varlist are not calculated. [...] A similar problem arises when the regressors include a variable that is a singleton dummy, i.e., a variable with one 1 and N-1 zeros or vice versa, if a robust covariance matrix is requested. The singleton dummy causes the robust covariance matrix estimator to be less than full rank. In this case, partialling-out the variable with the singleton dummy solves the problem. Question #1: I don't fully understand what happens when variables are "partialled out" and I couldn't find much literature on it. Could somebody point me to relevant resources on this? Is it even legit to use it in a standard 2sls model? The description of the options mentions how "by the Frisch-Waugh-Lovell (FWL) theorem, in IV, two-step GMM and LIML estimation the coefficients for the remaining regressors are the same as those that would be obtained if the variables were not partialled out." Does this hold for 2sls as well? More generally speaking, do you think it is okay for me to do this here? What are the consequences for my estimates. Problem #2: Because -ivreg- is an out-of-date command as of Stata 10, I now tried running my regressions with -ivregress-. What is different: * First of all, now the "singleton dummies - use partial out" error message does no longer occur. * Second, my first stage and therefore my second stage no longer have significant effects in a limited sample (in my bigger sample it still works). Question #2: Why does the error message not occur anymore? And under the condition that it even makes sense to partial out (i.e. question #1) could I somehow do it with ivregress? Answers to my questions will be greatly appreciated. Thanks a lot! Best, Max * * 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/

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