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In modern economic literature, the most common measure of a taxpayer's loss from a distortionary tax, such as a tax on bicycles, is the equivalent variation, the maximum amount that a taxpayer would be willing to forgo in a lump sum to avoid the tax. The deadweight loss can then be interpreted as the difference between the equivalent variation ...
Formal estimation models not tailored to a particular organization's own context, may be very inaccurate. Use of own historical data is consequently crucial if one cannot be sure that the estimation model's core relationships (e.g., formula parameters) are based on similar project contexts.
There are several open source projects that provide similar data version control capabilities to DVC, [52] such as: Git LFS, Dolt, Nessie, and lakeFS. These projects vary in their fit to the different needs of data engineers and data scientists such as: scalability, supported file formats, support in tabular data and unstructured data, volume ...
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because these are simply the most common patterns found in the data. A simple review of the above table should make these rules obvious. The support for Rule 1 is 3/7 because that is the number of items in the dataset in which the antecedent is A and the consequent 0. The support for Rule 2 is 2/7 because two of the seven records meet the ...
A common position in economics is that the costs in a cost-benefit analysis for any tax-funded project should be increased according to the marginal cost of funds, because that is close to the deadweight loss that will be experienced if the project is added to the budget, or to the deadweight loss removed if the project is removed from the budget.
Because the data collection and preparation process for the input–output accounts is necessarily labor and computer intensive, input–output tables are often published long after the year in which the data were collected—typically as much as 5–7 years after. Moreover, the economic "snapshot" that the benchmark version of the tables ...
Mixed-data sampling (MIDAS) is an econometric regression developed by Eric Ghysels with several co-authors. There is now a substantial literature on MIDAS regressions and their applications, including Ghysels, Santa-Clara and Valkanov (2006), [ 1 ] Ghysels, Sinko and Valkanov, [ 2 ] Andreou, Ghysels and Kourtellos (2010) [ 3 ] and Andreou ...