Block Bootstrap Theory for Multivariate Integrated and Cointegrated Processes

Jentsch, Carsten; Paparoditis, Efstathios; Politis, Dimitris N.

doi:10.1111/jtsa.12088

Cited by 6 publications

(3 citation statements)

References 31 publications

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“…One may expect that appropriate asymptotic results can also be obtained for such a set-up given the results in Cavaliere, Rahbek & Taylor (2010) and Jentsch et al (2014). To be precise, a joint central limit theorem on the relevant estimators corresponding to Theorem 3.1 as well as a proof of the asymptotic validity of the MBB applied to residuals obtained from an estimated vector error correction model is required.…”

Section: Discussionmentioning

confidence: 99%

Inference in VARs with conditional heteroskedasticity of unknown form

Brüggemann

Jentsch

Trenkler

2016

Journal of Econometrics

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115

120

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We derive a framework for asymptotically valid inference in stable vector autoregressive (VAR) models with conditional heteroskedasticity of unknown form. We prove a joint central limit theorem for the VAR slope parameter and innovation covariance parameter estimators and address bootstrap inference as well. Our results are important for correct inference on VAR statistics that depend both on the VAR slope and the variance parameters as e.g. in structural impulse response functions (IRFs). We also show that wild and pairwise bootstrap schemes fail in the presence of conditional heteroskedasticity if inference on (functions) of the unconditional variance parameters is of interest because they do not correctly replicate the relevant fourth moments' structure of the error terms. In contrast, the residual-based moving block bootstrap results in asymptotically valid inference. We illustrate the practical implications of our theoretical results by providing simulation evidence on the finite sample properties of different inference methods for IRFs. Our results point out that estimation uncertainty may increase dramatically in the presence of conditional heteroskedasticity.Moreover, most inference methods are likely to understate the true estimation uncertainty substantially in finite samples.JEL classification: C30, C32

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Section: Discussionmentioning

confidence: 99%

Inference in VARs with conditional heteroskedasticity of unknown form

Brüggemann

Jentsch

Trenkler

2016

Journal of Econometrics

Self Cite

115

120

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show abstract

“…Although Theorem 3.2 considers two special cases: the WB and the i.i.d. bootstrap method that resample as in (42) and (43), respectively, the result extends to any bootstrap method that resamples the vector êt over t.…”

Section: If In Addition Plimmentioning

confidence: 68%

Estimating the variance of a combined forecast: Bootstrap-based approach

Hounyo

Lahiri

2023

Journal of Econometrics

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“…For the asymptotic theory associated multivariate block bootstraps and block length selection criteria, seeJentsch et al (2015).…”

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confidence: 99%

Three Essays in Econometrics

Richard¹

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In this thesis, I contribute to the literature on multiple comparisons and specification testing for multivariate models, through the lens of model selection procedures in asset pricing.In the first chapter, I provide a model selection procedure for multivariate models, generalizing the model confidence set (MCS) procedure to systems of N > 1 dependent variables. A (1 − α) level MCS collects the set of models with equal predictive ability, based on a sequential elimination procedure that relies on an equivalence test.I introduce supremum-type t and Hotelling-type T 2 statistics which account for correlation between loss differentials. I assess the performance of 14 candidate asset pricing models using monthly data for the period 1972-2013. I find that for out-of-sample tests, only a single model is ever selected by the procedure, but the MCS often includes multiple models for in-sample tests. Overall, out-of-sample tests and a larger number of more heterogenous test assets provide more information to disentangle models. The procedure shows good size and power properties in simulations.I thank my committee members, Ba Chu, Maral Kichian, and Matthew Webb for the insightful discussions and feedback they have provided. I also thank the faculty members and the administrative staff at the Economics department, for their support and encouragement throughout my PhD. Louis-Philippe Beland and Konstantinos Metaxoglou deserve a special mention for their tremendous help during the job market season.Finally, I would like to thank my wife, Sappaya, without whose endless support and patience completing my PhD would not have been possible.

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Block Bootstrap Theory for Multivariate Integrated and Cointegrated Processes

Cited by 6 publications

References 31 publications

Inference in VARs with conditional heteroskedasticity of unknown form

Inference in VARs with conditional heteroskedasticity of unknown form

Estimating the variance of a combined forecast: Bootstrap-based approach

Three Essays in Econometrics

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