Optimal Bandwidth Selection in Heteroskedasticity–Autocorrelation Robust Testing

Sun, Yixiao; Phillips, Peter C.B.; Jin, Sainan

doi:10.1111/j.0012-9682.2008.00822.x

Cited by 195 publications

(160 citation statements)

References 14 publications

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“…The upper bound on the error rate of the fixed-b asymptotics we derive here when p = ∞ is not as fast as the rates found by Jansson (2004) and Sun et al (2008). Thus, the bound on the ERP we provide is not sharp.…”

Section: Introductioncontrasting

confidence: 62%

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

Gonçalves¹,

Vogelsang²

2011

Econom. Theory

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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,

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Section: Introductioncontrasting

confidence: 62%

“…Recently, Jansson (2004) and Sun, Phillips and Jin (2008) analyzed the higher order asymptotic properties of the fixed-b asymptotic theory for the simple location model when the data is Gaussian.…”

Section: Introductionmentioning

confidence: 99%

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

Gonçalves¹,

Vogelsang²

2011

Econom. Theory

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“…Analogously to arguments employed by Sun, Phillips, and Jin (2008) will also depend on the covariance between S X (0) AM , respectively S X (0) NW , and the OLS estimates.…”

Section: Cev-nwmentioning

confidence: 99%

Are Spectral Estimators Useful for Implementing Long-Run Restrictions in SVARs?

Mertens

2010

SSRN Journal

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No, not really, since spectral estimators suffer from small sample and misspecification biases just as VARs do. Spectral estimators are no panacea for implementing long-run restrictions.In addition, when combining VAR coefficients with non-parametric estimates of the spectral density, care needs to be taken to consistently account for information embedded in the non-parametric estimates about serial correlation in VAR residuals. This paper uses a spectral factorization to ensure a correct representation of the data's variance. But this cannot overcome the fundamental problems of estimating the long-run dynamics of macroeconomic data in samples of typical length.JEL Classification: C32, C51, E17, E32

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“…To ensure the positive semi-definiteness of the resulting estimators, appropriate weights, like the Bartlett sequence, are necessary. For more details on this issue and alternative estimators, see Andrews (1991), Gallant and White (1988), Kiefer and Vogelsang (2005), West (1987, 1994), and Sun et al (2008). It can be easy to show the regularity conditions that are sufficient for the consistency of these covariance matrix estimators are met in the present case.…”

Section: Confidence Bands For Roc Curvementioning

confidence: 84%

Confidence Bands for ROC Curves With Serially Dependent Data

Lahiri

Liu

2018

Journal of Business & Economic Statistics

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We propose serial correlation robust asymptotic confidence bands for the receiver operating characteristic (ROC) curves estimated by quasi-maximum likelihood in the binormal model. Our simulation experiments confirm that this new method performs fairly well in finite samples. The conventional procedure is found to be markedly undersized in terms of yielding empirical coverage probabilities lower than the nominal level, especially when the serial correlation is strong. We evaluate the three-quarter-ahead probability forecasts for real GDP declines from the Survey of Professional Forecasters, and find that one would draw a misleading conclusion about forecasting skill if serial correlation is ignored. JEL Classifications: C01, C12, C13, C53Key words: Receiver operating characteristic curve, Classification, Binormal model, Quasi-maximum likelihood, Survey of Professional Forecasters, Serial correlation. * Corresponding author. Tel.: +1 518 442 4758. E-mail address: klahiri@albany.edu. † We thank Zhongwen Liang for carefully reading an earlier version of the paper and making valuable comments and suggestions.

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Optimal Bandwidth Selection in Heteroskedasticity–Autocorrelation Robust Testing

Cited by 195 publications

References 14 publications

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

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

Are Spectral Estimators Useful for Implementing Long-Run Restrictions in SVARs?

Confidence Bands for ROC Curves With Serially Dependent Data

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