The fast iterated bootstrap

Davidson, Russell; Trokić, Mirza

doi:10.1016/j.jeconom.2020.04.025

Cited by 4 publications

(3 citation statements)

References 40 publications

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“…Thus, one replication on the bootstrap in the second stage is enough for each first-stage bootstrap data set. This method produces the same level of accuracy as the double bootstrap method but requires a much shorter processing time (Davidson & Trokić, 2020).…”

Section: Introductionmentioning

confidence: 97%

“…Different bootstrap methods have been developed for different types of regression, such as residuals bootstrap, block bootstrap (Djogbenou, MacKinnon, & Nielsen, 2019), wild bootstrap, and wild cluster bootstrap (Canay, Santos, & Shaikh, 2021). The block bootstrap is used in time series models and deals with panel data models and heteroscedasticity (Davidson & Trokić, 2020). Then, the pairs bootstrap is used in dynamic or heteroscedastic models in which the error term is unknowingly distributed (Lütkepohl & Schlaak, 2019).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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

Ngabu,

Pramoedyo,

Fitriani

et al. 2023

ComTech

View full text Add to dashboard Cite

show abstract

“…By considering Bootstrap iteration as a Markov process, Kenny et al (2001) proposed an algorithm to improve the accuracy of bootstrap with small sample bias reduced, but the computational complexity is still too large. Recently, a new double Bootstrap of Davidson and MacKinnon has been extended, and the method makes computational demands that increase only linearly with the level of iteration (Davidson and Trokic, 2020). Chen et al combined Bootstrap with GM(1, 1) model to verify online data under the influence of dynamic uncertainty (Chen et al , 2015).…”

Section: Introductionmentioning

confidence: 99%

Simulation evaluation of small samples based on grey estimation and improved bootstrap

Wei-guo

Lin

Gao

2021

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PurposeTo solve the problem of simulation evaluation with small samples, a fresh approach of grey estimation is presented based on classical statistical theory and grey system theory. The purpose of this paper is to make full use of the difference of data distribution and avoid the marginal data being ignored.Design/methodology/approachBased upon the grey distribution characteristics of small sample data, the definition about a new concept of grey relational similarity measure comes into being. At the same time, the concept of sample weight is proposed according to the grey relational similarity measure. Based on the new definition of grey weight, the grey point estimation and grey confidence interval are studied. Then the improved Bootstrap resampling is designed by uniform distribution and randomness as an important supplement of the grey estimation. In addition, the accuracy of grey bilateral and unilateral confidence intervals is introduced by using the new grey relational similarity measure approach.FindingsThe new small sample evaluation method can realize the effective expansion and enrichment of data and avoid the excessive concentration of data. This method is an organic fusion of grey estimation and improved Bootstrap method. Several examples are used to demonstrate the feasibility and validity of the proposed methods to illustrate the credibility of some simulation data, which has no need to know the probability distribution of small samples.Originality/valueThis research has completed the combination of grey estimation and improved Bootstrap, which makes more reasonable use of the value of different data than the unimproved method.

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