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Nowcasting Vat Data in the Retail Trade Sector Using Historical Data and Electronic Payment Data.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Nowcasting Vat Data in the Retail Trade Sector Using Historical Data and Electronic Payment Data./
作者:	Neto, Moyses Xavier Fontoura.
面頁冊數:	1 online resource (71 pages)
附註:	Source: Masters Abstracts International, Volume: 85-11.
Contained By:	Masters Abstracts International85-11.
標題:	Statistics. -
電子資源:	click for full text (PQDT)
ISBN:	9798382506968

Nowcasting Vat Data in the Retail Trade Sector Using Historical Data and Electronic Payment Data.
Neto, Moyses Xavier Fontoura.

Nowcasting Vat Data in the Retail Trade Sector Using Historical Data and Electronic Payment Data. - 1 online resource (71 pages)

Source: Masters Abstracts International, Volume: 85-11.

Thesis (M.S.)--Universidade do Porto (Portugal), 2023.

Includes bibliographical references

Timely information about the current state of the economy is essential, as it influences the population's input and output choices, and allows the government to react as quickly as possible to economic events that need intervention. Given the importance of timely information, EUROSTAT has regulated the Short-term business statistics (STS), setting deadlines, quality requirements, and other guidelines for their publication.One of the STS published by the National Statistical Institute (INE) is the Retail Trade Turnover Index (RTTI), which is broken down into different economic activities classifications (CAEs). The estimation of the RTTI relies on important data provided by the Tax Authority -the e-Fatura data. However, sometimes the Tax Authority fails to deliver the data in time, which poses a threat to the compliance of the quality requirements of this early index.To mitigate this risk, INE decided to build a framework to estimate in a timely manner (nowcast) this data for whenever it is not delivered in time again. To this end, in addition to historical e-Fatura data, Multibanco data is used as an auxiliary variable to nowcast the e-Faturadata. The models used were ARIMA, Linear regression, Dynamic regression, MIDAS regression and the mean of these four models' nowcasts.After analysing the relationship between the response and the auxiliary variables, the nowcasting exericise was carried out. The results obtained showed that there is not a single modelthat can nowcast the e-Fatura data for all CAEs with the best accuracy. Although the modelsthat used the Multibanco data as an auxiliary variable had the expectation to perform better thanthe classical ARIMA approach, the model that performed better for almost half of the CAEs wasthe ARIMA model, followed by the Linear regression, the mean, the Dynamic regression and the MIDAS regression.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382506968Subjects--Topical Terms:

556824
Statistics.
Index Terms--Genre/Form:

554714
Electronic books.

Nowcasting Vat Data in the Retail Trade Sector Using Historical Data and Electronic Payment Data.
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100 1 $a Neto, Moyses Xavier Fontoura. $3 1473679
245 1 0 $a Nowcasting Vat Data in the Retail Trade Sector Using Historical Data and Electronic Payment Data.
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300 $a 1 online resource (71 pages)
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500 $a Source: Masters Abstracts International, Volume: 85-11.
500 $a Advisor: Silva, Maria Eduarda.
502 $a Thesis (M.S.)--Universidade do Porto (Portugal), 2023.
504 $a Includes bibliographical references
520 $a Timely information about the current state of the economy is essential, as it influences the population's input and output choices, and allows the government to react as quickly as possible to economic events that need intervention. Given the importance of timely information, EUROSTAT has regulated the Short-term business statistics (STS), setting deadlines, quality requirements, and other guidelines for their publication.One of the STS published by the National Statistical Institute (INE) is the Retail Trade Turnover Index (RTTI), which is broken down into different economic activities classifications (CAEs). The estimation of the RTTI relies on important data provided by the Tax Authority -the e-Fatura data. However, sometimes the Tax Authority fails to deliver the data in time, which poses a threat to the compliance of the quality requirements of this early index.To mitigate this risk, INE decided to build a framework to estimate in a timely manner (nowcast) this data for whenever it is not delivered in time again. To this end, in addition to historical e-Fatura data, Multibanco data is used as an auxiliary variable to nowcast the e-Faturadata. The models used were ARIMA, Linear regression, Dynamic regression, MIDAS regression and the mean of these four models' nowcasts.After analysing the relationship between the response and the auxiliary variables, the nowcasting exericise was carried out. The results obtained showed that there is not a single modelthat can nowcast the e-Fatura data for all CAEs with the best accuracy. Although the modelsthat used the Multibanco data as an auxiliary variable had the expectation to perform better thanthe classical ARIMA approach, the model that performed better for almost half of the CAEs wasthe ARIMA model, followed by the Linear regression, the mean, the Dynamic regression and the MIDAS regression.
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