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Asymptotic Size of Kleibergen's LM and Conditional LR Tests for Moment Condition Models

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Abstract

An influential paper by Kleibergen (2005) introduces Lagrange multiplier (LM) and conditional likelihood ratio-like (CLR) tests for nonlinear moment condition models. These procedures aim to have good size performance even when the parameters are unidentified or poorly identified. However, the asymptotic size and similarity (in a uniform sense) of these procedures has not been determined in the literature. This paper does so. This paper shows that the LM test has correct asymptotic size and is asymptotically similar for a suitably chosen parameter space of null distributions. It shows that the CLR tests also have these properties when the dimension p of the unknown parameter theta equals 1: When p > 2; however, the asymptotic size properties are found to depend on how the conditioning statistic, upon which the CLR tests depend, is weighted. Two weighting methods have been suggested in the literature. The paper shows that the CLR tests are guaranteed to have correct asymptotic size when p > 2 with one weighting method, combined with the Robin and Smith (2000) rank statistic. The paper also determines a formula for the asymptotic size of the CLR test with the other weighting method. However, the results of the paper do not guarantee correct asymptotic size when p > 2 with the other weighting method, because two key sample quantities are not necessarily asymptotically independent under some identification scenarios. Analogous results for confidence sets are provided. Even for the special case of a linear instrumental variable regression model with two or more right-hand side endogenous variables, the results of the paper are new to the literature.

Suggested Citation

  • Donald W. K. Andrews & Patrik Guggenberger, 2014. "Asymptotic Size of Kleibergen's LM and Conditional LR Tests for Moment Condition Models," Cowles Foundation Discussion Papers 1977, Cowles Foundation for Research in Economics, Yale University.
  • Handle: RePEc:cwl:cwldpp:1977
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    File URL: http://cowles.yale.edu/sites/default/files/files/pub/d19/d1977-a.pdf
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    1. Davidson, James, 1994. "Stochastic Limit Theory: An Introduction for Econometricians," OUP Catalogue, Oxford University Press, number 9780198774037.
    2. de Jong, Robert M., 1997. "Central Limit Theorems for Dependent Heterogeneous Random Variables," Econometric Theory, Cambridge University Press, vol. 13(03), pages 353-367, June.
    3. Andrews, Donald W.K., 1988. "Laws of Large Numbers for Dependent Non-Identically Distributed Random Variables," Econometric Theory, Cambridge University Press, vol. 4(03), pages 458-467, December.
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    Cited by:

    1. repec:eee:econom:v:199:y:2017:i:2:p:96-116 is not listed on IDEAS
    2. Cheng, Xu, 2015. "Robust inference in nonlinear models with mixed identification strength," Journal of Econometrics, Elsevier, vol. 189(1), pages 207-228.
    3. Phillips, Peter C.B. & Gao, Wayne Yuan, 2017. "Structural inference from reduced forms with many instruments," Journal of Econometrics, Elsevier, vol. 199(2), pages 96-116.

    More about this item

    Keywords

    Asymptotics; Conditional likelihood ratio test; Confidence set; Identification; Inference; Lagrange multiplier test; Moment conditions; Robust; Test; Weak identification; Weak instruments;

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General

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