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Non-standard errors
In: IWH discussion papers 2021, no. 11 (November 2021)
In statistics, samples are drawn from a population in a datagenerating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidencegenerating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.
Non-Standard Errors
In: Tinbergen Institute Discussion Paper 2021-102/IV
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Non-Standard Errors in Carbon Premia
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Working paper
Finite Population Causal Standard Errors
In: NBER Working Paper No. w20325
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Corrected Standard Errors with Clustered Data
In: Political analysis: PA ; the official journal of the Society for Political Methodology and the Political Methodology Section of the American Political Science Association, Band 28, Heft 3, S. 318-339
ISSN: 1476-4989
The use of cluster robust standard errors (CRSE) is common as data are often collected from units, such as cities, states or countries, with multiple observations per unit. There is considerable discussion of how best to estimate standard errors and confidence intervals when using CRSE (Harden 2011; Imbens and Kolesár 2016; MacKinnon and Webb 2017; Esarey and Menger 2019). Extensive simulations in this literature and here show that CRSE seriously underestimate coefficient standard errors and their associated confidence intervals, particularly with a small number of clusters and when there is little within cluster variation in the explanatory variables. These same simulations show that a method developed here provides more reliable estimates of coefficient standard errors. They underestimate confidence intervals for tests of individual and sets of coefficients in extreme conditions, but by far less than do CRSE. Simulations also show that this method produces more accurate standard error and confidence interval estimates than bootstrapping, which is often recommended as an alternative to CRSE.
Clustering Standard Errors at the "Session" Level
In: CESifo Working Paper No. 8386
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Working paper
Standard Errors of Age-Specific Fertility Rates
In: Artha Vijnana: Journal of The Gokhale Institute of Politics and Economics, Band 4, Heft 4, S. 343
Standard Errors for Predictive Regression with Overlapping Observations
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A Note on HAC Standard Errors for Regression Forecast Errors
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Corrected standard errors for optimal minimum distance estimator
In: Economics letters, Band 167, S. 5-9
ISSN: 0165-1765