Testing Parameters in GMM without Assuming that they are identified

By F. Kleibergen. 2005

412 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/54b7297bd4d07dc788b3197f106e4457a036f750

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Citations in the corpus

Monetary Policy Rules and Macroeconomic Stability: Some New Evidence (2010, methodology): “So, following Stock and Wright (2000) and Kleibergen (2005), they are replaced by the restricted estimates ̂ 0 ; 0 x and ̂i 0 ; 0 x : 7The K-LM con dence set reported here is actually based on the combination of a 9% level K-LM and a 1% JKLM test, as suggested by Kleibergen (2005).”
Instrumental variable quantile regression: A robust inference approach (2008, methodology): “In addition, our new inferential approach is a useful complement to important work on inference in weakly identified settings by, for example, Andrews, Moreira, and Stock (2006), Kleibergen (2005), Kleibergen (2002), and Moreira (2003).”
Robust Standard Errors in Transformed Likelihood Estimation of Dynamic Panel Data Models (2015, positive): “Under the null hypothesis H 0 : δ = δ 0 , this statistic follows χ 22 , asymptotically The GMM version of the CLR statistic proposed by Kleibergen (2005) is given by where R ( δ ) is a statistic which is large when instruments are strong and small when the instruments are weak, and is random only…”
Robust Inference for Consumption‐Based Asset Pricing (2019, positive): “In Kleibergen, Kong, and Zhan (2019), extensions of other weak identification-robust tests, like, the Lagrange multiplier and likelihood ratio tests (see, e.g., Kleibergen (2002, 2005) and Moreira (2003)) to this setting are discussed.”
The Weak Instrument Problem of the System GMM Estimator in Dynamic Panel Data Models (2010, neutral): “Although the data are not generated as in the cross-section model (13), we can write the structural equation and the reduced form model for the AR(1) panel data model in first differences for the cross-section at time t as”
GMM with many weak moment conditions (2005, methodology): “The last result shows that the Kleibergen (2005) K-statistic has the usual chi-squared distribution: Theorem 5: If Assumptions 1, 2, and 4-6 are satisfied Λn −→ Λ∗ and β0 = β̄ then T̂ (β̄) d−→ χ2(p).”
ON THE ASYMPTOTIC SIZE DISTORTION OF TESTS WHEN INSTRUMENTS LOCALLY VIOLATE THE EXOGENEITY ASSUMPTION (2011, positive): “Kleibergen (2005) suggests the K test, a modification of the AR test aimed at improving the power properties in overidentified models.”
GENERALIZED EMPIRICAL LIKELIHOOD ESTIMATORS AND TESTS UNDER PARTIAL, WEAK, AND STRONG IDENTIFICATION (2003, methodology): “Secondly, following Kleibergen’s (2001) suggestion of constructing a statistic from the FOC with respect to θ but in the GMM framework, we construct a test statistic based on the GEL FOC for bθ.”
RELIABLE INFERENCE FOR GMM ESTIMATORS? FINITE SAMPLE PROPERTIES OF ALTERNATIVE TEST PROCEDURES IN LINEAR PANEL DATA MODELS (2005, methodology): “12See also the test procedures proposed by Kleibergen (2001) and Moreira (2001).”
Generic results for establishing the asymptotic size of confidence sets and tests (2020, background): “…and Andrews and Guggenberger (2012); for weak identification due 27 to weak IV’s, Staiger and Stock (1997), Stock and Wright (2000), Moreira (2003), Kleibergen (2005), Guggenberger et al. 28 (2012a), Guggenberger et al. (2012b, 2019); for weak identification in other models, Andrews and Cheng…”
A new method of projection-based inference in GMM with weakly identified nuisance parameters (2011, methodology): “…where the subscript n denotes the sample size.1 We also note that, under the null hypothesis H1 : θ1 = θ∗1, a confidence region for the nuisance parameters θ2 can be obtained by inverting any weak identification robust test; e.g. the S test, the K test or the GMM-MLR test [see Kleibergen (2005)].”
Generalizing weak instrument robust IV statistics towards multiple parameters, unrestricted covariance matrices and identification statistics (2007, methodology): “A number of novel statistics have been proposed that overcome the difficulties associated with the traditional IV statistics: Stock and Wright (2000) introduce the S-statistic, which is based on the Anderson–Rubin (AR) (1949) statistic, Kleibergen (2002, 2005) constructs a score or Lagrange multip”