A regularization approach to the many instruments problem

By M. Carrasco. 2012

140 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a99a26d7d775d332bec41fcfa52210b32c3fed0d

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Citations in the corpus

The Welfare Effects of Peer Entry in the Accommodation Market: The Case of Airbnb (2018, unknown): “A complementary paper to ours is Cohen et al. (2016), which uses discontinuities in Uber’s surge pricing policy to estimate the consumer surplus from ride sharing.”
ON THE ASYMPTOTIC EFFICIENCY OF GMM (2013, methodology): “Carrasco (2012) investigates two other regularizations for the estimation of a linear instrumental variables model: one iterative method called Landweber Fridman and another consisting in using the eigenfunctions of K that are associated with the largest eigenvalues, called spectral cut-off.”
Select the Valid and Relevant Moments: An Information-Based LASSO for GMM with Many Moments (2013, background): “Carrasco (2012) studies e¢ cient estimation with many IVs by regularization techniques.”
High-dimensional econometrics and regularized GMM (2018, background): “See, for example, [41], [100], [37], and [71] for approaches that explicitly use regularized first-stage estimation within a dense model framework.”
Valid Post-Selection and Post-Regularization Inference: An Elementary, General Approach (2015, unknown): “oecients structure over rst-stage coecients, and Okui (2010) uses ridge regression for estimating the rst-stage regression in a framework where the instruments may be ordered in terms of relevance. Carrasco (2012) employs a dierent strategy based on directly regularizing the inverse that appears in ”
Instrumental variables estimation with many weak instruments using regularized JIVE (2014, methodology): “Under the conditions of Carrasco (2012) this correlation is asymptotically negligible and thus does not adversely affect the asymptotic properties of the regularized IV estimator.”
Machine Learning: An Applied Econometric Approach (2017, background): “In particular, a set of papers has already introduced regularization into the first stage in a high-dimensional setting, including the LASSO (Belloni, Chen, Chernozhukov, and Hansen 2012) and ridge regression (Carrasco 2012; Hansen and Kozbur 2014).”
INSTRUMENTAL VARIABLE ESTIMATION IN A DATA RICH ENVIRONMENT (2010, methodology): “Amemiya (1966) and more recently Carrasco (2006) provided different statistical justifications for the approach without reference to a factor structure.”
Testing Conditional Independence Via Empirical Likelihood (2014, methodology): “Because we have an infinite number of conditional moment conditions, it seems impossible to handle them once and for all in a single maximization problem without resorting to some regularization techniques of the sort used by Carrasco (2010). We thus consider one conditional moment restriction at a ”
Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain (2012, methodology): “Carrasco (2012) provides an interesting approach to IV estimation with many instruments based on directly regularizing the inverse that appears in the definition of the 2SLS estimator; see also Carrasco and Tchuente Nguembu (2012).”
Instrumental regression in partially linear models (2012, unknown): “It is of course possible to consider another scheme of regularization (see Carrasco, Florens, and Renault (2007)).”
Instrumental variable estimation in the presence of many moment conditions (2011, background): “here.4 Another related paper is Carrasco (2008) .”