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Implementing intersection bounds in Stata

  • Victor Chernozhukov

    ()

    (Institute for Fiscal Studies and MIT)

  • Wooyoung Kim
  • Sokbae 'Simon' Lee

    ()

    (Institute for Fiscal Studies and Seoul National University)

  • Adam Rosen

    ()

    (Institute for Fiscal Studies and University College London)

We present the clrbound, clr2bound, clr3bound and clrtest commands for estimation and inference developed by Chernozhukov et al. (2013). The commands clrbound, clr2bound and clr3bound provide bound estimates that can be used directly for estimation or to construct asymptotically valid confidence sets. The command clrbound provides bound estimates for one-sided lower or upper intersection bounds on a parameter, while clr2bound and clr3bound provide two-sided bound estimates based on both lower and upper intersection bounds. clr2bound uses Bonferroni's inequality to construct two-sided bounds, whereas clr3bound inverts a hypothesis test. The former can be used to perform asymptotically valid inference on the identified set or the parameter, while the latter can be used to provide asymptotically valid and generally tighter confidence intervals for the parameter. clrtest performs an intersection bound test of the hypothesis that a collection of lower intersection bounds is no greater than zero. Inversion of this test can be used to construct confidence sets based on conditional moment inequalities as described in Chernozhukov et al. (2013). The commands include parametric, series and local linear estimation procedures and can be installed from within Stata by typing 'ssc install clrbound'.

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File URL: http://www.cemmap.ac.uk/wps/cwp381313.pdf
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Paper provided by Centre for Microdata Methods and Practice, Institute for Fiscal Studies in its series CeMMAP working papers with number CWP38/13.

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Date of creation: Aug 2013
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Handle: RePEc:ifs:cemmap:38/13
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  1. Victor Chernozhukov & Sokbae 'Simon' Lee & Adam Rosen, 2011. "Intersection bounds: estimation and inference," CeMMAP working papers CWP34/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  2. Pedro Carneiro & Sokbae 'Simon' Lee, 2009. "Estimating distributions of potential outcomes using local instrumental variables with an application to changes in college enrollment and wage inequality," CeMMAP working papers CWP01/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  3. Ben Jann, 2005. "MOREMATA: Stata module (Mata) to provide various functions," Statistical Software Components S455001, Boston College Department of Economics, revised 17 May 2014.
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