Bootstrap Methods for Time Series

Kreiß, Jens-Peter; Lahiri, Soumendra N.

doi:10.1016/b978-0-444-53858-1.00001-6

Cited by 97 publications

(71 citation statements)

References 90 publications

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“…The bootstrap method is widely used to estimate sampling distributions of statistics [1]. We will show here that the frequency-domain bootstrap (FDB) for time series analysis [2,Sect. 6] is well suited for estimating uncertainty in the modeling parameters arising in the theory of nuclear binding energies.…”

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confidence: 99%

Estimating Parameter Uncertainty in Binding-Energy Models by the Frequency-Domain Bootstrap

Bertsch¹,

Bingham²

2017

Phys. Rev. Lett.

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We propose using the frequency-domain bootstrap (FDB) to estimate errors of modeling parameters when the modeling error is itself a major source of uncertainty. Unlike the usual bootstrap or the simple χ 2 analysis, the FDB can take into account correlations between errors. It is also very fast compared to the the Gaussian process Bayesian estimate as often implemented for computer model calibration. The method is illustrated with a simple example, the liquid drop model of nuclear binding energies. We find that the FDB gives a more conservative estimate of the uncertainty in liquid drop parameters in better accord with more empirical estimates. For the nuclear physics application, there no apparent obstacle to apply the method to the more accurate and detailed models based on density-functional theory.

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confidence: 99%

Estimating Parameter Uncertainty in Binding-Energy Models by the Frequency-Domain Bootstrap

Bertsch¹,

Bingham²

2017

Phys. Rev. Lett.

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“…Thus, time series data must be resampled indirectly. A very recent and good review about bootstrap for time series is, for example, that of Kreiss and Lahiri (2012). In the context of INAR processes, to the best of our knowledge, we found only few papers about bootstrap and INAR(p) model.…”

Section: Bootstrap For Inar(p) Modelsmentioning

confidence: 99%

“…In this work we choose l = √ n. For the other several variants of block bootstrap and further details, see Kreiss and Lahiri (2012) and the reference therein.…”

Section: Block Bootstrapmentioning

confidence: 99%

Estimation and Forecasting in INAR(P) Models Using Sieve Bootstrap

Bisaglia

Gerolimetto

2018

SSRN Journal

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In this paper we analyse some bootstrap techniques to make inference in INAR(p) models. First of all, via Monte Carlo experiments we compare the performances of these methods when estimating the thinning parameters in INAR(p) models. We state the superiority of sieve bootstrap approaches on block bootstrap in terms of low bias and Mean Square Error (MSE). Then we apply the sieve bootstrap methods to obtain coherent predictions and confidence intervals in order to avoid difficulty in deriving the distributional properties. Keywords INAR(p) models, estimation, forecast, bootstrap JEL Codes C22, C53 AbstractIn this paper we analyse some bootstrap techniques to make inference in INAR(p) models. First of all, via Monte Carlo experiments we compare the performances of these methods when estimating the thinning parameters in INAR(p) models. We state the superiority of sieve bootstrap approaches on block bootstrap in terms of low bias and Mean Square Error (MSE). Then we apply the sieve bootstrap methods to obtain coherent predictions and confidence intervals in order to avoid difficulty in deriving the distributional properties.

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“…Since the time series data is no more independent, we used the moving block bootstrap (MBB), where we divided the series into N overlapping blocks of length ℓ to preserve the dependence structure of the original dataset (Kreiss and Lahiri, 2012). Then we chose b blocks out of N blocks to make the bootstrap observations

{bold-italicy}_{1}^{*}, \dots, {bold-italicy}_{S}^{*}

.…”

Section: Numerical Studiesmentioning

confidence: 99%

Frequentist standard errors of Bayes estimators

2017

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Summary Frequentist standard errors are a measure of uncertainty of an estimator, and the basis for statistical inferences. Frequestist standard errors can also be derived for Bayes estimators. However, except in special cases, the computation of the standard error of Bayesian estimators requires bootstrapping, which in combination with Markov chain Monte Carlo (MCMC) can be highly time consuming. We discuss an alternative approach for computing frequentist standard errors of Bayesian estimators, including importance sampling. Through several numerical examples we show that our approach can be much more computationally efficient than the standard bootstrap.

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Bootstrap Methods for Time Series

Cited by 97 publications

References 90 publications

Estimating Parameter Uncertainty in Binding-Energy Models by the Frequency-Domain Bootstrap

Estimating Parameter Uncertainty in Binding-Energy Models by the Frequency-Domain Bootstrap

Estimation and Forecasting in INAR(P) Models Using Sieve Bootstrap

Frequentist standard errors of Bayes estimators

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