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HAC Corrections for Strongly Autocorrelated Time Series
Ulrich K. Müller
Economics
Bendheim Center for Finance
Center for Statistics & Machine Learning
Research output
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peer-review
60
Scopus citations
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Dive into the research topics of 'HAC Corrections for Strongly Autocorrelated Time Series'. Together they form a unique fingerprint.
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Mathematics
Autocorrelation
100%
Heteroscedasticity
100%
Smaller Sample
66%
Gaussian Distribution
33%
Weighted Average
33%
Earth and Planetary Sciences
Time Series
100%
Autocorrelation
100%
Keyphrases
AR(1) Model
33%
Power Criterion
33%