Empirical Likelihood Based Inference in Conditional Moment Restriction Models

By Y. Kitamura, Gautam Tripathi, Hyungtaik Ahn. 2004

228 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/14bd75c3182c18025ffe77283fe06e5adafa5a31

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Citations in the corpus

Nonparametric likelihood ratio model selection tests between parametric likelihood and moment condition models (2007, methodology): “…of Vuong (1989) for comparing two parametric models and of the nonparametric likelihood ratio test of Kitamura (2000) for comparing two moment-based 2cf. Kitamura and Stutzer (1997), Qin and Lawless (1994), Kitamura (1997), Imbens, Spady, and Johnson (1998) and Kitamura and Tripathi (2001). models.”
On the second-order properties of empirical likelihood with moment restrictions (2007, background): “In particular, Newey and Smith (2004) find that the EL estimator is favorable in terms of the bias and the second order variance in comparison with the GMM estimator.”
Adaptive Estimation of Autoregressive Models with Time-Varying Variances (2008, methodology): “JEL classification: C14; C22 Keywords: Adaptive estimation; Autoregression; Heterogeneity; Nonstationary volatility; Weighted regression”
A regularization approach to the many instruments problem (2012, methodology): “This remark leads us to consider alternative methods that do not require a ranking of the instruments and may be simpler to implement than the modi ed EL estimators of Kitamura et al (2004) and Donald et al (2003).”
Efficient information theoretic inference for conditional moment restrictions (2005, unknown): “Owen (1988), for ET, π̂ij = wij exp(λ̂ 0 iûj)/ Pn k=1wik exp(λ̂iûk), cf.”
Estimation with overidentifying inequality moment conditions (2009, unknown): “By and large, we follow the structure of the proofs in Kitamura, Tripathi, and Ahn (2004) and Newey and Smith (2004), making the necessary adjustments for the presence of the inequality moment conditions.”
EMPIRICAL LIKELIHOOD FOR GARCH MODELS (2006, result): “The results of this section can be extended to a higher order GARCH(r, s) model by means of the results in Ling and Li (1998). It is also straightforward to extend the results of this section to the nearly unit root case as in Chan and Wei (1987) and Chuang and Chan (2002).”
An updated review of Goodness-of-Fit tests for regression models (2013, methodology): “Some previous references on this methodology can be consulted in Tripathi and Kitamura (2003) and Kitamura et al. (2004), and an extension to multidimensional response models can be found in Chen and Van Keilegom (2009a).”
Nobel Lecture: Uncertainty Outside and Inside Economic Models (2014, methodology): “For instance, the analysis of GMM efficiency in Hansen ð1982b, 1985Þ and related liter- 16 Relatedly, Zhang and Gijbels ð2003Þ, Kitamura, Tripathi, and Ahn ð2004Þ, and Antoine, Bonnal, and Renault ð2007Þ studied methods based on restricting nonparametric estimates of conditional density functions…”