A bootstrap theory for weakly integrated processes

Park, Joon‐Young

doi:10.1016/j.jeconom.2005.06.009

Cited by 12 publications

(10 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, Park (2003) uses strong approximations to show that the bootstrap provides an asymptotic refinement for unit root tests. Similarly, Park (2006) relies on this method to show asymptotic refinements of the bootstrap in the context of weakly integrated processes. Our methods of proof will closely follow those of Park (2006).…”

Section: Higher-order Resultsmentioning

confidence: 99%

“…Similarly, Park (2006) relies on this method to show asymptotic refinements of the bootstrap in the context of weakly integrated processes. Our methods of proof will closely follow those of Park (2006).…”

Section: Higher-order Resultsmentioning

confidence: 99%

“…Since u * t are i.i.d. we can apply the strong approximation result of Sakhanenko (1980) to W * T , as in the proof of Lemma 2.4 of Park (2006). That is, we may choose W * T in the same probability space as the Brownian motionW * (r) = Ω * 1/2 W 1 (r) such that W * T has the same conditional distribution as W 0 * T and verifies the following condition:…”

Section: Appendix Bmentioning

confidence: 99%

See 2 more Smart Citations

Block Bootstrap Hac Robust Tests: The Sophistication of the Naive Bootstrap

Gonçalves¹,

Vogelsang²

2011

Econom. Theory

View full text Add to dashboard Cite

This paper studies the properties of naive block bootstrap tests that are scaled by zero frequency spectral density estimators (long run variance estimators). The naive bootstrap is a bootstrap where the formula used in the bootstrap world to compute standard errors is the same as the formula used on the original data. Simulation evidence shows that the naive bootstrap can be much more accurate than the standard normal approximation. The larger the HAC bandwidth, the greater the improvement. This improvement holds for a large number of popular kernels, including the Bartlett kernel, and it holds when the i.i.d. bootstrap is used and yet the data are serially correlated. Using recently developed fixed-b asymptotics for HAC robust tests, we provide theoretical results that can explain the finite sample patterns. We show that the block bootstrap, including the special case of the i.i.d. bootstrap, has the same limiting distribution as the fixed-b asymptotic distribution. For the special case of a location model, we provide theoretical results that suggest the naive bootstrap can be more accurate than the standard normal approximation depending on the choice of the bandwidth and the number of finite moments in the data. Our theoretical results lay the foundation for a bootstrap asymptotic theory that is an alternative to the traditional approach based on Edgeworth expansions. * For helpful comments and suggestions we thank an editor and two anonymous referees,

show abstract

Section: Higher-order Resultsmentioning

confidence: 99%

Section: Higher-order Resultsmentioning

confidence: 99%

Section: Appendix Bmentioning

confidence: 99%

See 1 more Smart Citation

Block Bootstrap Hac Robust Tests: The Sophistication of the Naive Bootstrap

Gonçalves¹,

Vogelsang²

2011

Econom. Theory

View full text Add to dashboard Cite

show abstract

“…Furthermore, they are applicable in a wide variety of estimation frameworks and work well in finite sample situations in which the prime objective is bias reduction. Although the bootstrap is often a viable candidate for bias reduction, it was shown by Park (2006) that the bootstrap is inconsistent in the presence of a near-unit root, and hence, jackknife methods offer a useful alternative in these circumstances.…”

Section: Introductionmentioning

confidence: 99%

Jackknife Bias Reduction in the Presence of a Near-Unit Root

Chambers

Kyriacou

2018

Econometrics

View full text Add to dashboard Cite

This paper considers the specification and performance of jackknife estimators of the autoregressive coefficient in a model with a near-unit root. The limit distributions of sub-sample estimators that are used in the construction of the jackknife estimator are derived, and the joint moment generating function (MGF) of two components of these distributions is obtained and its properties explored. The MGF can be used to derive the weights for an optimal jackknife estimator that removes fully the first-order finite sample bias from the estimator. The resulting jackknife estimator is shown to perform well in finite samples and, with a suitable choice of the number of sub-samples, is shown to reduce the overall finite sample root mean squared error, as well as bias. However, the optimal jackknife weights rely on knowledge of the near-unit root parameter and a quantity that is related to the long-run variance of the disturbance process, which are typically unknown in practice, and so, this dependence is characterised fully and a discussion provided of the issues that arise in practice in the most general settings.

show abstract

“…In addition, we make use of sieve bootstrap method, developed by Bühlman (1998), to implement the proposed test. Sieve bootstraps are known to be useful for time series data in econometrics context (cf: Park 2006). Conventional method requires estimation of asymptotic variance of the test, which is proportional to the spectral densities at zero frequency.…”

Section: Introductionmentioning

confidence: 99%

Nonparametric testing for long-horizon predictability with persistent covariates

Liu

2014

Journal of Nonparametric Statistics

View full text Add to dashboard Cite

We propose a testing procedure for long-horizon predictability via kernel-based nonparametric estimators of long-run covariances between multiperiod returns and persistent covariates. Asymptotic properties of the proposed tests are studied. As for implementation of the test, sieve bootstrap methods are employed to obtain reasonable approximation to the sample distribution of the test statistics. Monte Carlo simulations are conducted to verify the theoretical conjecture. Empirical analysis, using US monthly data from 1929 to 2011, are presented for testing stock return predictability of some forecasting financial variables. Long-term interest rates, unlike default spreads or price-earning ration, are found to show some forecasting power.

show abstract

A bootstrap theory for weakly integrated processes

Cited by 12 publications

References 15 publications

Block Bootstrap Hac Robust Tests: The Sophistication of the Naive Bootstrap

Block Bootstrap Hac Robust Tests: The Sophistication of the Naive Bootstrap

Jackknife Bias Reduction in the Presence of a Near-Unit Root

Nonparametric testing for long-horizon predictability with persistent covariates

Contact Info

Product

Resources

About