Block bootstrapping for a panel mean break test

Choi, Ji Eun; Shin, Dong Wan

doi:10.1007/s42952-019-00034-8

Cited by 3 publications

(9 citation statements)

References 32 publications

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“…As observed in [2], H ⋆CB and H ⋆SB does not exceed the theoretical size of 5% much under serial and under cross-sectional dependence. However, with the new data-adaptive block length choice, the test J ⋆RS is not oversized at least for mild serial dependence (ρ = 0.3), and the size distortion for stronger serial dependence (ρ = 0.5) is much less severe compared to J ⋆CS .…”

Section: Simulation Studysupporting

confidence: 59%

“…They have compared their test to several other proposed tests. We demonstrate that by an appropriate, data-adaptive choice of the block length, the changepoint test by Sharipov, Tewes, Wendler [7] can at least cope with mild temporal dependence, the size distortion of this test is not as severe as claimed by Choi and Shin [2].…”

mentioning

confidence: 85%

“…Table 1 shows the empirical rejection frequencies under the hypothesis of no change (all δ i = 0) for S = 1000 simulation runs. H ⋆CB and H ⋆SB denote the test of Horváth and Hušková [3] combined with the circular or the stationary bootstrap as implemented by Choi and Shin [2]. J ⋆CS denotes the test based on J N,T and nonoverlapping block bootstrap with the block length choice as proposed by Choi and Shin [2], while J ⋆RS uses the same test statistic, but with the block length choice using the method by Rice and Shang [6] as described in the previous section.…”

Section: Simulation Studymentioning

confidence: 99%

“…However, with the new data-adaptive block length choice, the test J ⋆RS is not oversized at least for mild serial dependence (ρ = 0.3), and the size distortion for stronger serial dependence (ρ = 0.5) is much less severe compared to J ⋆CS . So it turns out that the claim of Choi and Empirical size of level α = 0.05 tests N (0, 1) error terms t 5 error terms based on H N,T and of the tests J ⋆CS and J ⋆RS based on J N,T for different values of the correlation parameters (ρ i , β i ) and of the sample size (N , T ) (values for H ⋆CB , H ⋆SB , J ⋆CS taken from [2]).…”

Section: Simulation Studymentioning

confidence: 99%

“…Choi and Shin [2] have suggested a change-point test for panel data of the form (X i,t ) i=1,..,N,t=1,...,T , and allow possible temporal correlation (that means Cov(X i,t , X i,s ) = 0 for s = t) or cross sectional correlation (Cov(X i,t , X j,u ) = 0 for i = j). They combine a test statistic introduced by Horváth and Hušková [3] once with circular bootstrap introduced by Politis and Romano [4] and once with the stationary bootstrap [5].…”

Section: Introductionmentioning

confidence: 99%

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Block Length Choice for the Bootstrap of Dependent Panel Data -- a Comment on Choi and Shin (2020)

Wegner¹,

Wendler²

2021

Preprint

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Choi and Shin [2] have constructed a bootstrap-based test for change-points in panels with temporal and and/or cross-sectional dependence. They have compared their test to several other proposed tests. We demonstrate that by an appropriate, data-adaptive choice of the block length, the changepoint test by Sharipov, Tewes, Wendler [7] can at least cope with mild temporal dependence, the size distortion of this test is not as severe as claimed by Choi and Shin [2].

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Section: Simulation Studysupporting

confidence: 59%

mentioning

confidence: 85%

Section: Simulation Studymentioning

confidence: 99%

Section: Simulation Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Block Length Choice for the Bootstrap of Dependent Panel Data -- a Comment on Choi and Shin (2020)

Wegner¹,

Wendler²

2021

Preprint

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Spatial Modeling of Fixed Effect and Random Effect with Fast Double Bootstrap Approach

Ngabu,

Pramoedyo,

Fitriani

et al. 2023

ComTech

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The use of panel data on spatial regression has many advantages. However, testing the spatial dependency and parameter presumption generated in spatial regression of panel data becomes inaccurate when applied to regions with large numbers of small spatial units. One method of overcoming problems of small spatial unit sizes is the bootstrap method. The research aimed to combine cross-section and time-series panel data. The analysis was performed to extract information based on observations modified by the influences of space or location, known as spatial analysis of panels. The influence of location effects on spatial analysis was presented in the form of weighting. The research applied the Fast Double Bootstrap (FDB) method by modeling poverty rates on Flores Island. The results of the Hausman test show the right model, which is a random effect. Meanwhile, spatial dependency testing concludes spatial dependence and poverty modeling in Flores Island, which is more likely to be the Spatial Autoregressive Random (SAR) model. SAR random effect in modeling value has R2 of 77,38% and does not meet the normality assumption. SAR effect in modeling the FDB approach can explain the diversity of poverty rate in the Flores Island with 88,64% and meets residual normality assumptions. The analysis with the FDB approach on spatial panels shows better results than the common spatial panels.

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Cluster Fast Double Bootstrap Approach With Random Effect Spatial Modeling

Ngabu

Fitriani

Pramoedyo

et al. 2023

BAREKENG: J. Math. & App.

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Panel data is a combination of cross-sectional and time series data. Spatial panel analysis is an analysis to obtain information based on observations affected by the space or location effects. The effect of location effects on spatial analysis is presented in the form of weighting. The use of panel data in spatial regression provides a number of advantages, however, the spatial dependence test and parameter estimators generated in the spatial regression of data panel will be inaccurate when applied to areas with a small number of spatial units. One method to overcome the problem of small spatial unit size is the bootstrap method. This study used the fast double bootstrap (FDB) method by modeling the poverty rate in the Flores islands. The data used in the study was sourced from the BPS NTT Province website. The results of Hausman test show that the right model is Random effect. The spatial dependence test concludes that there is a spatial dependence and the poverty modeling in the Flores islands tends to use the SAR model. SAR random effect model R2 shows the value of 77.38 percent and it does not meet the assumption of normality. Spatial Autoregressive Random effect model with the Fast Double Bootstrap approach is able to explain the diversity of poverty rate in the Flores Island by 99.83 percent and fulfilling the assumption of residual normality. The results of the analysis using the FDB approach on the spatial panel show better results than the common spatial panel.

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Block bootstrapping for a panel mean break test

Cited by 3 publications

References 32 publications

Block Length Choice for the Bootstrap of Dependent Panel Data -- a Comment on Choi and Shin (2020)

Block Length Choice for the Bootstrap of Dependent Panel Data -- a Comment on Choi and Shin (2020)

Spatial Modeling of Fixed Effect and Random Effect with Fast Double Bootstrap Approach

Cluster Fast Double Bootstrap Approach With Random Effect Spatial Modeling

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