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Mincer–Zarnowitz quantile and expectile regressions for forecast evaluations under aysmmetric loss functions
Kemal Guler
, Pin T Ng
, Zhijie Xiao
Business, The W.A. Franke College of (CoB)
Research output
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Contribution to journal
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Article
›
peer-review
12
Scopus citations
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Dive into the research topics of 'Mincer–Zarnowitz quantile and expectile regressions for forecast evaluations under aysmmetric loss functions'. Together they form a unique fingerprint.
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Keyphrases
Quantile Regression
100%
Loss Function
100%
Expectile Regression
100%
Asymmetric Loss Function
100%
Forecast Evaluation
100%
Federal Reserve
66%
Forecast Error
66%
Diagnostic Test
33%
Popular
33%
Economic Growth
33%
Daily Life
33%
Root Mean Square Error
33%
Mean Absolute Error
33%
Economic Expectations
33%
Forecast Sharing
33%
Growth Forecasts
33%
Economic Forecasting
33%
Optimality Properties
33%
About Four
33%
Manufacturing Supply Chain
33%
Nursing and Health Professions
Consumer
100%
Generator
100%
Diagnostic Test
100%
Economics, Econometrics and Finance
Summary Statistic
100%
Economic Forecast
33%