Combination of Using Pairwise Comparisons and Composite Reference Series: A New Approach in the Homogenization of Climatic Time Series with ACMANT

The removal of non-climatic biases, so-called inhomogeneities, from long climatic records needs sophistically developed statistical methods. One principle is that the differences between a candidate series and its neighbor series are usually analyzed instead of the candidate series directly, in order to neutralize the possible impacts of regionally common natural climate variation on the detection of inhomogeneities. In most homogenization methods, two main kinds of time series comparisons are applied, i.e., composite reference series or pairwise comparisons. In composite reference series, the inhomogeneities of neighbor series are attenuated by averaging the individual series, and the accuracy of homogenization can be improved by the iterative improvement of composite reference series. By contrast, pairwise comparisons have the advantage that coincidental inhomogeneities affecting several station series in a similar way can be identified with higher certainty than with composite reference series. In addition, homogenization with pairwise comparisons tends to facilitate the most accurate regional trend estimations. A new time series comparison method is presented here, which combines the use of pairwise comparisons and composite reference series in a way that their advantages are unified. This time series comparison method is embedded into the Applied Caussinus-Mestre Algorithm for homogenizing Networks of climatic Time series (ACMANT) homogenization method, and tested in large, commonly available monthly temperature test datasets. Further favorable characteristics of ACMANT are also discussed.

Combination of Using Pairwise Comparisons and Composite Reference Series: A New Approach in the Homogenization of Climatic Time Series

The removal of non-climatic biases, so-called inhomogeneities, from long climatic records needs sophistically developed statistical methods. One principle is that usually the differences between a candidate series and its neighbour series are analysed instead of directly the candidate series, in order to neutralize the possible impacts of regionally common natural climate variation on the detection of inhomogeneities. In most homogenization methods, two main kinds of time series comparisons are applied, i.e. composite reference series or pairwise comparisons. In composite reference series the inhomogeneities of neighbour series are attenuated by averaging the individual series, and the accuracy of homogenization can be improved by the iterative improvement of composite reference series. By contrast, pairwise comparisons have the advantage that coincidental inhomogeneities affecting several station series in a similar way can be identified with higher certainty than with composite reference series. In addition, homogenization with pairwise comparisons tends to facilitate the most accurate regional trend estimations. A new time series comparison method is presented here, which combines the use of pairwise comparisons and composite reference series in a way that their advantages are unified. This time series comparison method is embedded into the ACMANT homogenization method, and tested in large, commonly available monthly temperature test datasets.

Auditors' Response to Auditor Business Risk: An Analysis Using Public and Private Companies

Using audit hours and hourly audit fees for a large sample of public and private companies, we examine how auditors respond to auditor business risk. We find that auditors work more hours and charge higher hourly fees when auditing public companies than when auditing private companies. A difference-in-differences time-series comparison of the pre- and post-initial public offering (IPO) periods also indicates that both audit hours and hourly audit fees are higher for the post-IPO period than for the pre-IPO period of the company. This suggests that auditors respond to an increase in auditor business risk by increasing audit effort and charging a risk premium for the residual risk that additional effort alone does not fully address.

Economic Development Effects of Major and Minor League Teams and Stadiums

Professional teams and leagues claim new stadiums lead to economic development. To test this, we utilize data from the Census Bureau on net establishment and employment changes across 871 Metropolitan and Micropolitan Statistical Areas from 2004 to 2012. Difference-in-differences and panel data techniques allow for a cross-sectional and time series comparison for both teams and new stadia in both professional and development leagues. Nearly all results from hundreds of models are insignificantly different from zero. Results from between- and random-effects models suggest that teams move into markets that already have higher employment and establishment growth. (JEL R58, H71, L83, Z28)