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Measuring bias is important as it helps identify flaws in quantitative forecasting methods or judgmental forecasts. It … can, therefore, potentially help improve forecasts. Despite this, bias tends to be under represented in the literature …: many studies focus solely on measuring accuracy. Methods for assessing bias in single series are relatively well known and …
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We investigate the accuracy of ex ante assessments of vulnerability to income poverty using cross-sectional data and panel data. We use long-term panel data from Germany and apply different regression models, based on household covariates and previous-year equivalence income, to classify a...
Persistent link: https://www.econbiz.de/10009615143
We investigate the accuracy of ex ante assessments of vulnerability to poverty using cross-sectional data and panel data. We use long-term panel data from Germany and apply different regression models, based on household covariates and previous-year equivalence income, to classify a household as...
Persistent link: https://www.econbiz.de/10009671469
We investigate the accuracy of ex ante assessments of vulnerability to income poverty using cross-sectional data and panel data. We use long-term panel data from Germany and apply different regression models, based on household covariates and previous-year equivalence income, to classify a...
Persistent link: https://www.econbiz.de/10010358150
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Tracking poverty is predicated on the availability of comparable consumption data and reliable price deflators. However, regular series of strictly comparable data are only rarely available. Poverty prediction methods that track consumption correlates as opposed to consumption itself have been...
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