Generalized method of trimmed moments

Čı́žek, Pavel

doi:10.1016/j.jspi.2015.11.004

Cited by 5 publications

(5 citation statements)

References 29 publications

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“…But there was only a limited number of papers trying to robustify the method, see e. g. [1,9,10,11,34,35,36,38,39,40,50,73] or [57] (and the references given there 5 ). Moreover, even these papers, including [66], consider the situation when the disturbances are homoscedastic, except of [7] where the idea of the generalized method of moments (GMM) is employed, see [26]. Due to the fact that GMM does not need to specify the underlying d. f., the method covers also the heteroscedasticity -in a latent way because GMM is in fact studied in the i. i. d. framework.…”

Section: Instrumental Variables As Alternative To the Ordinary Least mentioning

confidence: 99%

“…Due to the fact that GMM does not need to specify the underlying d. f., the method covers also the heteroscedasticity -in a latent way because GMM is in fact studied in the i. i. d. framework. So, although [7] does not address directly (in formalism) the heteroscedasticity, it is a way how to cope with it. On the other hand, as the heteroscedasticity is not implemented in the model, it is not explicitly treated which could (and in fact does) result in a bad efficiency of GMM, see e. g. Table 2 in [7].…”

Section: Instrumental Variables As Alternative To the Ordinary Least mentioning

confidence: 99%

“…So, although [7] does not address directly (in formalism) the heteroscedasticity, it is a way how to cope with it. On the other hand, as the heteroscedasticity is not implemented in the model, it is not explicitly treated which could (and in fact does) result in a bad efficiency of GMM, see e. g. Table 2 in [7]. Let's recall that the heteroscedasticity does not prevent the unbiasedness and the consistency ofβ (OLS,n) .…”

Section: Instrumental Variables As Alternative To the Ordinary Least mentioning

confidence: 99%

“…The problem may be also with the zero-one objective function (employed in [7]), i. e. with trimming the observations. It implies (nearly) inevitably an instability of estimator with respect to inliers, even for estimators with moderate level of robustness, see [30] and also [56].…”

Section: Instrumental Variables As Alternative To the Ordinary Least mentioning

confidence: 99%

“…7 Of course, not to all characters, otherwise it would not be a scientific method, see [45]. Now, denoting the indicator of a set A by I {A}, for any β ∈ R p the empirical distribution function (e. d. f.) of the absolute value of residual will be considered in the form…”

Section: Robustifying the Instrumental Variablesmentioning

confidence: 99%

See 4 more Smart Citations

Instrumental weighted variables under heteroscedasticity, Part I -- Consistency

Víšek¹

2017

Kybernetika

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Section: Instrumental Variables As Alternative To the Ordinary Least mentioning

confidence: 99%

Section: Instrumental Variables As Alternative To the Ordinary Least mentioning

confidence: 99%

Section: Instrumental Variables As Alternative To the Ordinary Least mentioning

confidence: 99%

Section: Instrumental Variables As Alternative To the Ordinary Least mentioning

confidence: 99%

Section: Robustifying the Instrumental Variablesmentioning

confidence: 99%

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Instrumental weighted variables under heteroscedasticity, Part I -- Consistency

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Innovation, Energy Consumption and Trade Dynamic: Evidence from Developed and Developing Countries

2023

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Robust Two-Step Wavelet-Based Inference for Time Series Models

Guerrier

Molinari

Victoria‐Feser

et al. 2021

Journal of the American Statistical Association

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Latent time series models such as (the independent sum of ) ARMA(p, q) models with additional stochastic processes are increasingly used for data analysis in biology, ecology, engineering, and economics. Inference on and/or prediction from these models can be highly challenging: (i) the data may contain outliers that can adversely affect the estimation procedure; (ii) the computational complexity can become prohibitive when the time series are extremely large; (iii) model selection adds another layer of (computational) complexity; and (iv) solutions that address (i), (ii), and (iii) simultaneously do not exist in practice. This paper aims at jointly addressing these challenges by proposing a general framework for robust two-step estimation based on a bounded influence M-estimator of the wavelet variance. We first develop the conditions for the joint asymptotic normality of the latter estimator thereby providing the necessary tools to perform (direct) inference for scale-based analysis of signals. Taking advantage of the model-independent weights of this first-step estimator, we then develop the asymptotic properties of two-step robust estimators using the framework of the generalized method of wavelet moments (GMWM). Simulation studies illustrate the good finite sample performance of the robust GMWM estimator and applied examples highlight the practical relevance of the proposed approach.

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Generalized method of trimmed moments

Cited by 5 publications

References 29 publications

Instrumental weighted variables under heteroscedasticity, Part I -- Consistency

Instrumental weighted variables under heteroscedasticity, Part I -- Consistency

Innovation, Energy Consumption and Trade Dynamic: Evidence from Developed and Developing Countries

Robust Two-Step Wavelet-Based Inference for Time Series Models

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