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Evaluating Probability Forecasts: Theory and Application to Macroeconomic Survey Data:
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MeanSGD 73.16 ( 47.49)¹ SGD 52.12 ( 33.84)¹ SGD 62.22 ( 40.39)¹
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1
Beck Dominik (?):

Evaluating Probability Forecasts (2014) (?)

ISBN: 9783639407556 (?) or 3639407555, in german, BLUES KIDS OF AMER 01/10/2014, Paperback, New

SGD 53.90 ( 34.99)¹ + Shipping: SGD 4.82 ( 3.13)¹ = SGD 58.72 ( 38.12)¹(without obligation)
From Seller/Antiquarian, Paperbackshop-US [8408184], Secaucus, NJ, U.S.A.
New Book. This item is printed on demand. Shipped from US This item is printed on demand.
2
Dominik Beck (?):

Evaluating Probability Forecasts (2014) (?)

ISBN: 9783639407556 (?) or 3639407555, in german, Av Akademikerverlag Mrz 2014, Paperback, New, reprint

SGD 50.75 ( 32.95)¹ + Shipping: SGD 23.87 ( 15.50)¹ = SGD 74.63 ( 48.45)¹(without obligation)
From Seller/Antiquarian, AHA-BUCH GmbH [51283250], Einbeck, NDS, Germany
This item is printed on demand - Print on Demand Titel. Neuware - Forecasting is of utmost importance in statistics. Besides the very popular point forecasts, there are probability forecasts which naturally incorporate the uncertainty associated with a prediction. Probability forecasts have first been used in meteorology, however, applications are manifold, E.g. in economics the Survey of Professional Forecasters (SPF). In order to determine 'good' probability forecasts, the concepts of sharpness and calibration are introduced. Calibration which is the statistical consistency between the forecasts and the observations, is assessed by the Probability Integral Transform (PIT). Sharpness measures the extent to which a probabilistic forecast is spread out and is assessed on the basis of so called scoring rules. These scoring rules are introduced along with some properties, the most important being (strict) propriety. Different scoring rules are introduced such as the quadratic score, the log score or the Ranked Probability Score. The empirical analysis is based on the SPF dataset. In addition to the single forecasts of the participants to the SPF, linear and logarithmic combinations with different weighting schemes of these single forecasts are analysed. Finally, statistical tests for equal predictice power in the sense of the well-known Diebold-Mariano test of these combinations are introduced and used to search for the best combination scheme. 92 pp. Englisch
3
Beck Dominik (?):

Evaluating Probability Forecasts (2012) (?)

ISBN: 9783639407556 (?) or 3639407555, in german, AV Akademikerverlag, Paperback, New, reprint

SGD 50.75 ( 32.95)¹ + Shipping: SGD 5.38 ( 3.49)¹ = SGD 56.13 ( 36.44)¹(without obligation)
From Seller/Antiquarian, English-Book-Service - A Fine Choice [1048135], Waldshut-Tiengen, Germany
This item is printed on demand for shipment within 3 working days.
4
Beck Dominik (?):

Evaluating Probability Forecasts (2014) (?)

ISBN: 9783639407556 (?) or 3639407555, in german, BLUES KIDS OF AMER 01/10/2014, Paperback, New

SGD 53.60 ( 34.80)¹ + Shipping: SGD 17.48 ( 11.35)¹ = SGD 71.08 ( 46.15)¹(without obligation)
From Seller/Antiquarian, Books2Anywhere [190245], Fairford, GLO, United Kingdom
New Book. Shipped from UK. This item is printed on demand.
9783639407556 - Beck, Dominik: Evaluating Probability Forecasts
5
Beck, Dominik (?):

Evaluating Probability Forecasts (?)

ISBN: 9783639407556 (?) or 3639407555, in german, Av Akademikerverlag, Paperback, New

SGD 50.75 ( 32.95)¹(free shipping, without obligation)
Free shipping
buecher.de GmbH & Co. KG, [1]
Forecasting is of utmost importance in statistics. Besides the very popular point forecasts, there are probability forecasts which naturally incorporate the uncertainty associated with a prediction. Probability forecasts have first been used in meteorology, however, applications are manifold, E.g. in economics the Survey of Professional Forecasters (SPF). In order to determine "good" probability forecasts, the concepts of sharpness and calibration are introduced. Calibration which is the statistical consistency between the forecasts and the observations, is assessed by the Probability Integral Transform (PIT). Sharpness measures the extent to which a probabilistic forecast is spread out and is assessed on the basis of so called scoring rules. These scoring rules are introduced along with some properties, the most important being (strict) propriety. Different scoring rules are introduced such as the quadratic score, the log score or the Ranked Probability Score. The empirical analysis is based on the SPF dataset. In addition to the single forecasts of the participants to the SPF, linear and logarithmic combinations with different weighting schemes of these single forecasts are analysed. Finally, statistical tests for equal predictice power in the sense of the well-known Diebold-Mariano test of these combinations are introduced and used to search for the best combination scheme.92 S. 220 mmVersandfertig in 3-5 Tagen, Softcover

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