Bootstrap and fast double bootstrap tests of cointegration rank with financial time series

Ahlgren, Niklas; Antell, Jan

doi:10.1016/j.csda.2008.03.025

Cited by 10 publications

(5 citation statements)

References 26 publications

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“…Fast double bootstrap (FDB, hereafter), which was independently proposed by White (2000) and MacKinnon (2000,2007), is an interesting alternative that only resamples once in the second stage of bootstrapping and can dramatically speed up the double bootstrap. The FDB has been applied to many tests in econometrics, see Davidson and MacKinnon (2002), Ahlgren and Antell (2008), Richard (2009), among others. Recently, Giacomini, Politis, and White (2013) applied the idea of FDB to reduce the computational cost in running Monte carlo experiments to assess the performance of bootstrap estimators and tests.…”

Section: Introductionmentioning

confidence: 99%

A Subsampled Double Bootstrap for Massive Data

Sengupta

Volgushev

Shao

2016

Journal of the American Statistical Association

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The bootstrap is a popular and powerful method for assessing precision of estimators and inferential methods. However, for massive datasets which are increasingly prevalent, the bootstrap becomes prohibitively costly in computation and its feasibility is questionable even with modern parallel computing platforms. Recently Kleiner, Talwalkar, Sarkar, and Jordan (2014) proposed a method called BLB (Bag of Little Bootstraps) for massive data which is more computationally scalable with little sacrifice of statistical accuracy. Building on BLB and the idea of fast double bootstrap, we propose a new resampling method, the subsampled double bootstrap, for both independent data and time series data. We establish consistency of the subsampled double bootstrap under mild conditions for both independent and dependent cases. Methodologically, the subsampled double bootstrap is superior to BLB in terms of running time, more sample coverage and automatic implementation with less tuning parameters for a given time budget. Its advantage relative to BLB and bootstrap is also demonstrated in numerical simulations and a data illustration.

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Section: Introductionmentioning

confidence: 99%

A Subsampled Double Bootstrap for Massive Data

Sengupta

Volgushev

Shao

2016

Journal of the American Statistical Association

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“…Remark The BaB outlined in Algorithm is related to the fast double bootstrap (FDB) co‐integration rank tests proposed in Ahlgren and Antell (). These tests are based on the FDB approach of Davidson and MacKinnon (); see also Beran ().…”

Section: Implementing Bias‐correctionmentioning

confidence: 99%

“…Moreover, the BaB uses the estimated bootstrap bias to directly modify the parameter estimates obtained from the original data, rather than correcting the bootstrap p-values. Moreover, the FDB outlined in Ahlgren and Antell (2008) is applied to the bootstrap cointegration rank tests of Swensen (2006), although it could, in principle, also be applied to the tests of Cavaliere et al (2012).…”

Section: Algorithm 2 (Bootstrap-after-bootstrap (Bab))mentioning

confidence: 99%

Bootstrap Co‐integration Rank Testing: The Effect of Bias‐Correcting Parameter Estimates

2015

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In this paper we investigate bootstrap-based methods for bias-correcting the first-stage parameter estimates used in some recently developed bootstrap implementations of the co-integration rank tests of Johansen (1996). In order to do so we adapt the framework of Kilian (1998) which estimates the bias in the original parameter estimates using the average bias in the corresponding parameter estimates taken across a large number of auxiliary bootstrap replications. A number of possible implementations of this procedure are discussed and concrete recommendations made on the basis of finite sample performance evaluated by Monte Carlo simulation methods. Our results show that bootstrap-based bias-correction methods can significantly improve upon the small sample performance of the bootstrap co-integration rank tests. A brief application of the techniques developed in this paper to international dynamic consumption risk sharing within Europe is also considered.

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“…Kilian (1998a, b), Kilian (1999), Lütkepohl (2000Lütkepohl ( , 2013, Benkwitz et al (2001), Lütkepohl et al (2015) for impulse response analysis, and for cointegration rank determination, van Giersbergen (1996), Swensen (2006Swensen ( , 2009, Ahlgren and Antell (2008), Cavaliere et al (2012). Inference based on the conventional IID bootstrap may be misleading in the presence of (conditional) heteroskedasticity.…”

Section: Introductionmentioning

confidence: 98%

Wild bootstrap tests for autocorrelation in vector autoregressive models

Ahlgren

Catani

2016

Stat Papers

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Tests for error autocorrelation (AC) are derived under the assumption of independent and identically distributed errors. The tests are not asymptotically valid if the errors are conditionally heteroskedastic. In this article we propose wild bootstrap (WB) Lagrange multiplier tests for error AC in vector autoregressive (VAR) models. We show that the WB tests are asymptotically valid under conditional heteroskedasticity of unknown form. WB tests based on a version of the heteroskedasticityconsistent covariance matrix estimator are found to have the smallest error in rejection probability under the null and high power under the alternative. We apply the tests to VAR models for credit default swap prices and Euribor interest rates. An important result that we find is that the WB tests lead to parsimonious models while the asymptotic tests suggest that a long lag length is required to get white noise residuals.

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Bootstrap and fast double bootstrap tests of cointegration rank with financial time series

Cited by 10 publications

References 26 publications

A Subsampled Double Bootstrap for Massive Data

A Subsampled Double Bootstrap for Massive Data

Bootstrap Co‐integration Rank Testing: The Effect of Bias‐Correcting Parameter Estimates

Wild bootstrap tests for autocorrelation in vector autoregressive models

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