LARGE SAMPLE PROPERTIES OF GENERALIZED METHOD OF

By Lars Peter Hansen. 1982

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Citations in the corpus

Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes (2019, methodology): “ent estimator for β or for ρπe. When the q-model is linear as above, this can be easily solved by instead applying any eﬃcient variant of the generalized method of moments (GMM), such as twostep GMM (Hansen, 1982; Hansen et al., 1996), to the set of moment equations given by mt(Hat;β)φti(Hat) = 0 ∀t”
Double/Debiased Machine Learning for Treatment and Causal Parameters (2016, background): “A= G and = Var P(m(W; 0; 0)]) = E P h m(W; 0; 0)m(W; 0; 0)0 i leads to the ecient score in the sense of yielding an estimator of 0 having the smallest variance in the class of GMM estimators (Hansen, 1982), and, in fact, to the semiparametrically ecient score; see Levit (1975), Nevelson (1977), an”
Mapping Transcriptomic Vector Fields of Single Cells (2022, unknown): “Instead of directly fitting the distribution, GMM seeks to solve the following equations of moments for , also known as moment conditions: where , , , ... are functions of the random variables (e.g. the copies of spliced and unspliced RNA across cells) and parameters .”
Stochastic estimation of firm technology, inefficiency, and productivity growth using shadow cost and distance functions (2002, unknown): “We address the validity of the overidentifying restrictions using the Hansen (1982) J test.”
Absorptive Capacity in High-Technology Markets: The Competitive Advantage of the Haves (2006, methodology): “Given more moment conditions than the number of parameters to estimate, the model is overidentified and one can check for the validity of the moment conditions using Hansen's J statistic (Hansen 1982).”
The MM, ME, ML, EL, EF and GMM approaches to estimation: a synthesis (2002, methodology): “Here, Hansen (1982) followed Sargan’s way of choosing (22)From our conversation with Lars Hansen and Christopher Sims, we learned that a rudimentary form of GMM estimator existed in Sims’ lecture notes.”
Estimation of a Panel Data Model with Parametric Temporal Variation in Individual Effects (2004, methodology): “Under certain regularity conditions (Hansen (1982), Assumption 3) the resulting GMM estimator is √ N -consistent and asymptotically normal.”
R&D—Productivity Dynamics: Causality, Lags, and ‘Dry Holes’ (2002, methodology): “Therefore, we resort to instrumental variable (IV) or generalized method of moments (GMM) estimators (Hansen 1982, White 1982).”
A Spatial Analysis of Sectoral Complementarity (2003, unknown): “Examples include Domowitz, Hubbard, and Petersen (1988), Burnside (1996), and Basu and Fernald (1997b). 16 More specifically, we require this relationship to hold in at least two of the four observations of the input-output use table in order for sector i output to instrument the sector j regression”
Capital Structure Dynamics and Transitory Debt (2011, unknown): “Despite the above discrepancies, when we use the χ2 test from Hansen (1982) of the null hypothesis that the expected value of the vector of moment conditions equals zero, we fail to find a rejection (p-value of 0.357, see panel B).”