Optimal Detection of Bilinear Dependence in Short Panels of Regression Data

Lmakri, A.; Akharif, Abdelhadi; Mellouk, Amal

doi:10.15446/rce.v43n2.83044

Cited by 4 publications

(4 citation statements)

References 29 publications

(33 reference statements)

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“…The uniform local asymptotic normality (ULAN) of model ( 1)-( 2) is established in [13] by using the quadratic mean differentiability of the density characterizing the model. Once the ULAN property is proved, the limit distribution and the asymptotic optimality of the locally asymptotically minimax (LAM) estimators are described in this latter.…”

Section: Estimation Methodsmentioning

confidence: 99%

Estimation in short-panel data models with bilinear errors

Lmakri¹,

Akharif²,

Mellouk³

2023

Math. Model. Comput.

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Many estimation methods have been proposed for the parameters of the regression models with serially correlated errors. In this work, we develop an asymptotic theory for estimation in the short panel data models with bilinear error. We propose a comparative study by simulation between several estimators (adaptive, ordinary and weighted least squares) for the coefficients of panel data models when the errors are bilinear serially correlated. As a consequence of the uniform local asymptotic normality property, we obtain adaptive estimates of the parameters. Finally, we illustrate the performance of the proposed estimators via Monte Carlo simulation study. We show that the adaptive estimates are more efficient than the weighted and ordinary least squares estimates.

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Section: Estimation Methodsmentioning

confidence: 99%

Estimation in short-panel data models with bilinear errors

Lmakri¹,

Akharif²,

Mellouk³

2023

Math. Model. Comput.

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“…This study employs a similar methodology to generalize the findings of previous works by [8,9]. It should be noted that our study focuses on testing the independence (white noise) assumption against panel super-diagonal bilinear dependence.…”

Section: Introductionmentioning

confidence: 92%

“…This powerful method has been used quite successfully in many inference problems; see [8,9,[17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Optimal Rank-based Procedures for Testing Randomness against Panel Bilinear Dependence

Lmakri,

Akharif,

Mellouk

2024

Preprint

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Based on the local asymptotic normality (LAN) property, we are proposing parametric and nonparametric locally and asymptotically optimal (in the Le Cam-Hájek sense) procedures for the problem of testing white noise against the general panel super-diagonal bilinear dependence. The proposed rank-based tests are asymptotically most stringent and valid under a wide class of densities. The asymptotic relative efficiencies (AREs), with respect to the corresponding pseudo-Gaussian procedure, are computed under various densities. Simulation studies are conducted to illustrate the finite-sample performance of the proposed tests.

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“…As an alternative to LR-test, Bayesian and permutation test, particularly concerning detecting the randomness in the coefficients of individual effects in longitudinal and clustered data, we present a parametric and non-parametric test locally and asymptotically optimal. Practical examples of a model building using Uniform Local Asymptotic Normality (ULAN) optimal test models can be found in [1,4,10,19] among many others.…”

Section: Introductionmentioning

confidence: 99%

Optimal tests for random effects in linear mixed models

Larbi

Halimi

Akharif

et al. 2021

Hacettepe Journal of Mathematics and Statistics

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In the past decade, mixed-effects modeling has received a great deal of attention in the applied and theoretical statistical literature. They are very flexible tools in analyzing repeated measures, panel data, cross-sectional data, and hierarchical data. However, the complex nature of these models has motivated researchers to study different aspects of this problem. One of which is to test the significance of random effects used to model unobserved heterogeneity in the population. The method of likelihood ratio test based on the normality assumption of the error term and random effects has been proposed. However, this assumption does not necessarily hold in practice. In this paper, we propose an optimal test based on the so-called uniform local asymptotic normality to detect the possible presence of random effects in linear mixed models. We show that the proposed test statistic is, consistent, locally asymptotically optimal even for a model that does not require the traditional assumption of normality and is comparable to the classical L.ratiotest when the standard assumptions are met. Finally, simulation studies and real data analysis are also conducted to empirically examine the performance of this procedure.

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Optimal Detection of Bilinear Dependence in Short Panels of Regression Data

Cited by 4 publications

References 29 publications

Estimation in short-panel data models with bilinear errors

Estimation in short-panel data models with bilinear errors

Optimal Rank-based Procedures for Testing Randomness against Panel Bilinear Dependence

Optimal tests for random effects in linear mixed models

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