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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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Contribution to journal
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Comment/debate
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peer-review
51
Scopus citations
Overview
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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
Heteroscedasticity
100%
Autocorrelation
84%
Small Sample
78%
Time series
72%
Long-run
48%
Weighted Average
46%
Standard error
42%
Regression
31%
Valid
30%
Review
29%
Simulation
25%
Context
25%
Coefficient
20%
Form
16%
Model
13%
Business & Economics
Heteroscedasticity
97%
Autocorrelation
85%
Small Sample
79%
Inference
67%
Standard Error
45%
Coefficients
30%
Simulation
27%
Social Sciences
time series
85%
simulation
32%
regression
25%