Empirical Likelihood Based Inference With Applications to Some Econometric Models

Bravo, Francesco

doi:10.1017/s0266466604202018

Cited by 25 publications

(14 citation statements)

References 26 publications

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“…The above-mentioned papers were concerned only with the null distribution. In line with Chen [33], Bravo [34], Mukerjee [35] and Chang and Mukerjee [36] for the third-order local power analyses, our arguments could be applied to the ED test statistic including the more general case of over-determined moment based models. The calculations involved in this case would, however, be extremely difficult.…”

Section: Resultsmentioning

confidence: 61%

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Comparison of Bartlett-type adjusted tests in the multiparameter case

Kakizawa

2010

Journal of Multivariate Analysis

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a b s t r a c tConsidering some Bartlett-type adjusted tests for a simple hypothesis about a multidimensional parameter, this paper clarifies similarities and dissimilarities with the oneparameter case developed in the 1990s, where a major emphasis is put on the issue posed by Rao and Mukerjee [C.R. Rao, R. Mukerjee, Comparison of Bartlett-type adjustments for the efficient score statistic, J. Statist. Plann. Inference 46 (1995) 137-146] on the power under a sequence of local alternatives. Not surprisingly, there is an infinite number of adjustments which extend Chandra-Mukerjee and Taniguchi approaches to the multiparameter case. Revisiting their ideas, this paper presents four specific cases (type K , K = 0, 1, 2, 3) and gives a sufficient condition under which our generalized adjustment for each case is uniquely determined, where type 0 is a counterpart of Chandra and Mukerjee's original proposal for Rao's test statistic, whereas the latter three types are introduced as double adjustments related to the Cordeiro and Ferrari approach. If the adjustment of type 1 is made instead of type K , K = 0, 2, 3, it is shown that Chandra and Mukerjee's approach is equivalent to Taniguchi's approach in terms of the third-order local power. The same is partially true for type 0, depending on the model under consideration. However, the adjustments of type K , K = 2, 3, reveal, in general, the non-equivalence of these two approaches in terms of the third-order local power.

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Section: Resultsmentioning

confidence: 61%

“…s ∈ N (see [37,34,24]). We believe that this approach is much easier than an earlier traditional proof in line with Chandra and Ghosh [44,45] (see also [43, …”

Section: Resultsmentioning

confidence: 99%

Comparison of Bartlett-type adjusted tests in the multiparameter case

Kakizawa

2010

Journal of Multivariate Analysis

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“…Bravo () showed an analogous result for ELR tests of linear parametric restrictions in the context of linear regression models. Bravo () showed an analogous result for ELR tests of simple parametric restrictions (i.e.

{normalH}_{0} : θ_{*} = θ_{0}

, where

θ_{*}

denotes the true parameter, for some known point θ 0 ) for the more general just‐identified moment restriction models.…”

Section: Introductionmentioning

confidence: 76%

Second-order refinement of empirical likelihood ratio tests of nonlinear restrictions

2017

The Econometrics Journal

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Summary In this paper, we investigate the second‐order properties of empirical likelihood ratio (ELR) tests of general nonlinear parametric restrictions for over‐identified moment restriction models. We derive the stochastic expansion of the ELR statistic for this very large class of testing problems and its formal distributional expansion. We show that we can improve the size properties of the ELR tests via either Bartlett correction or pseudo observation adjustment. Monte Carlo experiments show that tests based on these modified ELR statistics exhibit good finite‐sample properties.

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“…In addition to the KBB, other bootstrap methods already exist in the GEL literature. For instance, Bravo (2004) shows the higher-order correctness of the bootstrap for inference based on empirical likelihood with i.i.d. data, while Bravo (2005) shows consistency of the block bootstrap for empirical entropy test in times series regressions with strongly mixing data.…”

Section: Introductionmentioning

confidence: 99%

A Higher-Order Correct Fast Moving-Average Bootstrap for Dependent Data

2019

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We develop and implement a novel fast bootstrap for dependent data. Our scheme is based on the i.i.d. resampling of the smoothed moment indicators. We characterize the class of parametric and semi-parametric estimation problems for which the method is valid. We show the asymptotic refinements of the proposed procedure, proving that it is higher-order correct under mild assumptions on the time series, the estimating functions, and the smoothing kernel. We illustrate the applicability and the advantages of our procedure for Generalized Empirical Likelihood estimation. As a by-product, our fast bootstrap provides higher-order correct asymptotic confidence distributions. Monte Carlo simulations on an autoregressive conditional duration model provide numerical evidence that the novel bootstrap yields higher-order accurate confidence intervals. A real-data application on dynamics of trading volume of stocks illustrates the advantage of our method over the routinely-applied first-order asymptotic theory, when the underlying distribution of the test statistic is skewed or fat-tailed.JEL classification: C12, C15, C22, C52, C58, G12.

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Empirical Likelihood Based Inference With Applications to Some Econometric Models

Cited by 25 publications

References 26 publications

Comparison of Bartlett-type adjusted tests in the multiparameter case

Comparison of Bartlett-type adjusted tests in the multiparameter case

Second-order refinement of empirical likelihood ratio tests of nonlinear restrictions

A Higher-Order Correct Fast Moving-Average Bootstrap for Dependent Data

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