Sentence examples for estimates of skill from inspiring English sources

Section 5 reports comparable estimates of skill mismatch and skill under- and over-utilization across the countries covered in PIAAC, for the entire population and for various subgroups.

Indications that 20 may be too small a number for robust estimates of skill can be found in studies, e.g., by Müller et al. [ 2005] who showed that robust results for the seasonal forecast skill of the NAO index were not stable with a sample size of 20 and by Kumar [ 2009] demonstrating the effect on skill measures of small verification time series due to sampling error.

In addition, we also develop a general procedure to construct standard errors for estimates of skill-mismatch derived from surveys like PIAAC, where the sampling frames can differ substantially across countries and where the test scores are derived from imputation models.

The 19th-century father of baseball statistics, Henry Chadwick, argued way back in 1868 that "it is in the record of his good plays that we are to look for the most correct data for an estimate of skill".

That is, just by taking away domain-specific measurement error in the PIAAC test, the estimate of skill returns increases by 10% in the IV model, suggesting that downward bias from measurement error may indeed be an important issue in the analysis of returns to skills.

For a perfect forecast system, the RPC should be close to 1. RPC values greater than one imply that the model is unduly pessimistic in its estimate of skill, by being overdispersive.

Despite reducing the size of the estimates of cognitive skills, cognitive skills remain highly significant and the main determinant of individual earnings after this (over) correction of PIAAC test scores.

Controlling for the measure of self-assessed health status available in PIAAC, though, barely changes the estimate of skills on earnings [row (7) of Table 1].

Bossa also provides support for dealing with the differences in the skills of volunteers by maintaining estimates of the skill level of volunteers and ensuring that for each task, there is a consensus of compatible results among a sufficient set of volunteers [34].

One of the difficulties with seasonal prediction research is that the sample size from which estimates of forecast skill can be obtained is necessarily small: for start dates at a given time of year, the sample size of seasonal forecasts for boreal winter from the 20 year period 1992 2011, as used in Eade et al. [ 2014] is just 20.

Keane and Wolpin (1997) estimated rates of skill attrition due to unemployment and show that white collar workers lose about 30% if they become long-term unemployed and blue collar workers 10%.