【正文】 此外它 對有關股票市場上的分歧影響假說給予解釋，即在假設 條件下 披露 了 因變量，但它沒有 數(shù)據(jù) 。 丁等人的文件是 否是問題和分歧的情況 都 有效的。 我 對存在某些分 歧的（如美國或英國）國家詳細的規(guī)則或超過 2021年的國際會計準則的限制 進行了分析。 他們的結論是，會計準則、國際會計準則的主題在許多國家是對股市疲軟和集中的所有權是有說服力的，確實是在諾貝斯會計規(guī)則（ 1998 年）發(fā)展的一般模式。 因此，他們的整個小節(jié)是可疑的 。 股市 在制定一個從國際會計準則的影響分歧變量假設 時， 丁等 人 討論穩(wěn)健的股本為一個國家的國際會計準則披露類似數(shù)量的需要。 日本已經從前者向后者。諾貝斯（ 1998）討論了這一點，并得出結論認為，財務報告的數(shù)量和風格影響的數(shù)量和審計員的作用。關于整個會計議題規(guī)則的情況下很可能是一個主要的分歧，特別是在實踐中的原因。 然而，在 X 國，而公司將國際 會計準則的做法確實偏離假設公司遵守的規(guī)則，在 Y 國（并在 Z公司）與國際會計準則可能大部分（即使用先進先出）相一致。最后，重要的問題是，是否會計做法 在 企業(yè)之間的國內或國際 具有 可比。這可能構成進一步系統(tǒng)化偏見，因為，例如，美國將深奧的分歧點多（見 段）是故意 引 入第二類 中 。首先，由于經濟不復雜的國家不需要最復雜的規(guī)則，對發(fā)展中國家的 “ 缺席 ” 數(shù)可能是沒有實際意義。在公認會計準則 2021 年，我們抵制誘惑，新增項目在一起，因為這兩個項目是顯然不同的重要性 。 有這樣一些國家盡量減少差異列表而將其他一些最大化。39。 評論：這就是差異的國家名單中正是為了避免原有的問題。 國家的高級技術人員隊伍回答的問題。 由于這些類型的差異不是由公認會計準則所涵蓋 2021 年，他們并沒有包括丁等人（因為他們注意到在他們的附錄 A）。如果是后者的話，那么，在法律上是由國際會計準則的差異性研究較少有關。事實上，后者可能是沒有實際意義（例如，如果公司利用國家規(guī)則選擇不使用后進先出）。例如，如果一個國家的規(guī)則并不需要一個特定的項目要公開，但公司往往在實踐中披露的話，那么這種“無規(guī)則”或許應被忽略。 規(guī)則 除了在 PWdata，一個國家的偏見問題進一步指出諾貝斯（ 1981 年），是在規(guī)則（法律上的差異）的差異與那些與實踐（事實上分歧混合）。因此，調查的目的不是為了使國際比較更遑論為學術研究提供數(shù)據(jù)。 目的是什么？ 丁等人以前的研究，其中包括，通過達科斯塔等數(shù)據(jù) （ 1978） ，弗蘭克（ 1979年）和奈爾 Frank（ 1980） ， 諾貝斯（ 1981）較早前指出，這是危險的， 因為除其他問題進行學術研究這些數(shù)據(jù)，他們的目的不是為宗旨。 由于數(shù)據(jù)的要求（以下簡稱“公認會計準則 2021”），我就其性質和它在學術上的研究，在這里使用丁等人的。 [關鍵字 ]:國際會計的差異 規(guī)則與慣例 數(shù)據(jù)偏差 丁等人 于 （ 2021 年）使用諾貝斯的數(shù)據(jù)（ 2021 年），以評估決定因素和國內和國際會計標準（ IAS）的差異影響。 and UK GAAP did not allow LIFO whereas IAS did. Since these types of difference are not covered by GAAP 2021, they were not included by Ding et al. (as they note in their Appendix A). If these differences were included, it would make the US and the UK look more different from IAS than the ??absence” and ??divergence” measures suggest, but it would not much affect the position of the Netherlands, where the rules were generally less detailed or less restrictive than IAS. . 111 topics but 79 survey questions Ding et al. (in Appendix A) notice that some topics in the survey do not correspond with the original survey questions asked. This is because the survey results were prepared after an interactive process. First, I prepared the questions by analysing the whole of IAS, assessing which were its key requirements. Country teams of senior technical staff replied to the questions. I asked for clari?cations, often disputing country answers. Sometimes, new issues turned up. Consensus was eventually reached, and the country teams signed off on the lists of differences. This also explains another feature of GAAP 2021 upon which Ding et al. ment: that the country lists of differences are not in exactly the order of the original questions. They are broadly in the order of: (i) consolidation issues, (ii) assets, and (iii) liabilities. However, an exact order is less important if there is no intention to pare countries. Incidentally, researchers using other data might consider contacting the preparers of the data in order to ask questions such as these. . Respondent behavior Another possible bias in the data is behavioral. Some countries like to be seen to be ??international” and therefore to be plying with IAS. This includes many developing countries. By contrast, in 2021 (before the Enron/Andersen debacle), the US was emphasizing that its accounting was different from (. better than) IAS (. Bloomer, 1999). There was thus pressure from some countries to minimize the list of differences and from a few others to maximize it. I believe that we did not give way in the former case, but readers of the US entry in GAAP 2021 might notice that some of the ??divergence” from IAS is abstruse, meaning that US divergence is exaggerated. 3. Methodology This section contains some speci?c observations on the paper by Ding et al. (2021), under three headings. . Additivity Ding et al. need to add items together so as to create scores for countries in order to perform numerical analyses. In GAAP 2021, we resisted the temptation to add items together because the items are clearly of differing importance. The above Sections and mention some examples of this. It would require a great deal of work and subjectivity to weight items according to importance. Not surprisingly, Ding et al. did not do it. However, this might introduce systematic biases. First, because less ?nancially plex countries do not need the most plex rules, several of the ??absences” in developing countries might be of no practical importance. So, the ??absence” scores for those countries are exaggerated. Secondly, as noted earlier, Ding et al. reduce the problem of additivity by creating two distinct to tals: absence and divergence. Nevertheless, they add the two ??inconsistency” categories together, despite the attempt in GAAP 2021 to suggest that the second category wa