Quantifying the data-dredging bias in structural break tests

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the washback effect of discretepoint vs. integrative tests on the retention of content in knowledge tests

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15 صفحه اول

Data Dredging

Suppose there is a given data set and there are a huge number of hypotheses [1] about this data set [2]. If the data is totally random, then say all the hypotheses are actually false. However, owing to the sheer number of hypotheses on a limited data set, it is possible to see some very highly correlated [3] data that are statistically significant [4]. In such cases, data dredging is said to ha...

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Data Dredging

Suppose there is a given data set and there are a huge number of hypotheses [1] about this data set [2]. If the data is totally random, then say all the hypotheses are actually false. However, owing to the sheer number of hypotheses on a limited data set, it is possible to see some very highly correlated [3] data that are statistically significant [4]. In such cases, data dredging is said to ha...

متن کامل

Data Dredging

Suppose there is a given data set and there are a huge number of hypotheses [1] about this data set [2]. If the data is totally random, then say all the hypotheses are actually false. However, owing to the sheer number of hypotheses on a limited data set, it is possible to see some very highly correlated [3] data that are statistically significant [4]. In such cases, data dredging is said to ha...

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Unit Root Tests for Time Series with a Structural Break When the Break Point Is Known

Unit root tests for time series with level shifts are considered. The level shift is assumed to occur at a known time point. In contrast to some other proposals the level shift is modeled as part of the intercept term of the stationary component of the data generation process which is separated from the unit root component. In this framework simple shift functions result in a smooth transition ...

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ژورنال

عنوان ژورنال: Statistical Papers

سال: 2021

ISSN: 0932-5026,1613-9798

DOI: 10.1007/s00362-021-01233-4