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Nowcasting Vat Data in the Retail Tr...

Neto, Moyses Xavier Fontoura.

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

Record Type:	Electronic resources : Monograph/item
Title/Author:	Nowcasting Vat Data in the Retail Trade Sector Using Historical Data and Electronic Payment Data./
Author:	Neto, Moyses Xavier Fontoura.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	71 p.
Notes:	Source: Masters Abstracts International, Volume: 85-11.
Contained By:	Masters Abstracts International85-11.
Subject:	Decomposition. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30984562
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. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 71 p.

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

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

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.

ISBN: 9798382506968Subjects--Topical Terms:

3561186
Decomposition.

Nowcasting Vat Data in the Retail Trade Sector Using Historical Data and Electronic Payment Data.
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500 $a Advisor: Silva, Maria Eduarda.
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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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