Inference on Causal and Structural Parameters using Many Moment Inequalities

• Victor Chernozhukov ^[2] ; Denis Chetverikov ^[3] ; Kengo Kato ^[1]
  1. [1] Cornell University

     Cornell University

     City of Ithaca, Estados Unidos

     
  2. [2] MIT
  3. [3] UCLA

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• Localización: Review of economic studies, ISSN 0034-6527, Vol. 86, Nº 5, 2019, págs. 1867-1900
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • This article considers the problem of testing many moment inequalities where the number of moment inequalities, denoted by p, is possibly much larger than the sample size n. There is a variety of economic applications where solving this problem allows to carry out inference on causal and structural parameters; a notable example is the market structure model of Ciliberto and Tamer (2009) where p=2m+1 with m being the number of firms that could possibly enter the market. We consider the test statistic given by the maximum of p Studentized (or t-type) inequality-specific statistics, and analyse various ways to compute critical values for the test statistic. Specifically, we consider critical values based upon (1) the union bound combined with a moderate deviation inequality for self-normalized sums, (2) the multiplier and empirical bootstraps, and (3) two-step and three-step variants of (1) and (2) by incorporating the selection of uninformative inequalities that are far from being binding and a novel selection of weakly informative inequalities that are potentially binding but do not provide first-order information. We prove validity of these methods, showing that under mild conditions, they lead to tests with the error in size decreasing polynomially in n while allowing for p being much larger than n; indeed p can be of order exp(nc) for some c>0. Importantly, all these results hold without any restriction on the correlation structure between p Studentized statistics, and also hold uniformly with respect to suitably large classes of underlying distributions. Moreover, in the online supplement, we show validity of a test based on the block multiplier bootstrap in the case of dependent data under some general mixing conditions.