Fine granular data data improves economic prediction accuracy
April 24, 2024 09:39 Source: "China Social Sciences", April 24, 2024, Issue 2879 Author: Liu Yuwei/Compilation

  bet365 Play online games Accurate assessment and predictive armed conflict、The impact of crisis such as infectious diseases or natural disasters on the economy,Help government governments take measures in time to reduce damage。"PNAS NEXUS" recently published a papers using the aggregate data and more detailed grain data (Granula Data), respectively.,and found that the estimation results obtained by different types of data are huge differences。

Fine granular data refers to fragmented、Data as segmented as possible,It can adapt to containers like sand grains,The needs of data scientists or Bet365 lotto review analysts are arbitrarily shaped or processed。The main author of the paper,Austrian Vienna Complex Science Center Network Economy、Christian DIEM, a senior scientist of the supply chain and the financial market group, said,Fine granularity data is a key component of a reasonable prediction of a dynamic economic system。Dim and others based on the actual experience data of the new crown epidemic in early 2020 and the micro -data of VAT in Hungary in 2019,Simulated 1,000 crisis simulation scenes,Analysis comparison the different results of the impact of crisis on the economy according to different situations。

VAT is a consumption tax levied for goods and services。From raw material production to finished product sales,It levy tax on every value -added link of the entire industrial chain。Dim and others collected Bet365 app download in 2019 Hungarian Central Bank VAT micro data, covering 243399 companies,Reconstructs 1104141 to supply relationship,Almost present the economic situation of the entire Hungary。The Chairman of the Vienna Complex Science Center Stefan Thurner said,With this data set,Researchers can better understand after the crisis,The interconnection and influence of each company in the economy。

So far,Researchers mostly explain and analyze the economic situation of a country from the economic sector level。Dim and others discovered,If the researchers use 88 economic departments defined by the EU as usual,So in 1,000 crisis simulation scenarios,The economic impact of each crisis will be systematically underestimated by 37%。Comparison of comparison,Economic impact results calculated by using the supply chain data at the bet365 Play online games company level,The result of the actual recession of the European economic recession in the second quarter of 2020。Dim inference this,When predicting the impact of crisis on the economy at the economic department level,People usually come to conclusions that are more conservative than the actual consequences of the company level。

Dim summarizes,Crisis simulation experiments based on fine -grained data such as VAT information data have multiple effects。It not only helps researchers to master the average loss of the entire economic department,It can also calculate the degree of the crisis affected by individual companies in a certain department。For details,,Crisis simulation experiments can reveal which companies may be seriously affected,Which is slightly affected,instead of the warning of a certain crisis, it will cause Bet365 app download a 20%economic loss to a certain economic department。More importantly,Researchers can understand the way and ways of transmission of crisis in the supply network,and its effect on direct and indirect trading partners。

Dim thinks,In principle,People can use the VAT system to restore the supply network of a country。As of now,More than 160 countries have a VAT system,But only a dozen countries are continuously collecting data that can be used to build a supply network,Including Spain、Belgium、Hungary、India and China。For countries that have not collected VAT data in Germany and Austria, etc.,Dim Suggestion,Small -scale adjustment in response to the company's value -added tax report,This will also reduce VAT fraud,Digital transition to promote the financial accounting of enterprises。

(Liu Yuwei/Compilation)

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