Journal ArticleDOI
The Size-Power Tradeoff in HAR Inference
TLDR
Higher-order expansions are used to provide a unified size-power frontier for both kernel and weighted orthonormal series tests using nonstandard “fixed-b” critical values, derived for the Gaussian multivariate location model, but simulations suggest the qualitative findings extend to stochastic regressors.Abstract:
Heteroskedasticity and autocorrelation-robust (HAR) inference in time series regression typically involves kernel estimation of the long-run variance. Conventional wisdom holds that, for a given kernel, the choice of truncation parameter trades off a test’s null rejection rate and power, and that this tradeoff differs across kernels. We formalize this intuition: using higher-order expansions, we provide a unified size-power frontier for both kernel and weighted orthonormal series tests using nonstandard “fixed-b” critical values. We also provide a frontier for the subset of these tests for which the fixed-b distribution is t or F. These frontiers are respectively achieved by the QS kernel and equal-weighted periodogram. The frontiers have simple closed-form expressions, which upon evaluation show that the price paid for restricting attention to tests with t and F critical values is small. The frontiers are derived for the Gaussian multivariate location model, but simulations suggest the qualitative findings extend to stochastic regressors.read more
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References
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Journal ArticleDOI
Handbook of Mathematical Functions
ReportDOI
A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix
Whitney K. Newey,Kenneth D. West +1 more
TL;DR: In this article, a simple method of calculating a heteroskedasticity and autocorrelation consistent covariance matrix that is positive semi-definite by construction is described.
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Spectral Analysis and Time Series
TL;DR: In this article, the authors introduce the concept of Stationary Random Processes and Spectral Analysis in the Time Domain and Frequency Domain, and present an analysis of Processes with Mixed Spectra.
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Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation
TL;DR: Using these results, data-dependent automatic bandwidth/lag truncation parameters are introduced and asymptotically optimal kernel/weighting scheme and bandwidth/agreement parameters are obtained.
Journal ArticleDOI
Lag length selection and the construction of unit root tests with good size and power
Serena Ng,Pierre Perron +1 more
TL;DR: In this paper, a modified information criterion (MIC) with a penalty factor that is sample dependent was proposed to select appropriate truncation lag values for unit root tests with a moving-average root close to -1.
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