國立虎尾科技大學 |

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

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Nowcasting Vat Data in the Retail Trade Sector Using Historical Data and Electronic Payment Data./
Author:	Neto, Moyses Xavier Fontoura.
Description:	1 online resource (71 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-11.
Contained By:	Masters Abstracts International85-11.
Subject:	Decomposition. -
Online resource:	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:

1372547
Decomposition.
Index Terms--Genre/Form:

554714
Electronic books.

Nowcasting Vat Data in the Retail Trade Sector Using Historical Data and Electronic Payment Data.
LDR:03154ntm a22003617 4500 001 1147851
005 20240916075414.5
006 m o d
007 cr bn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798382506968
035 $a (MiAaPQ)AAI30984562
035 $a (MiAaPQ)Portugal10216153542
035 $a AAI30984562
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
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.
264 0 $c 2023
300 $a 1 online resource (71 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Decomposition. $3 1372547
650 4 $a Stochastic models. $3 683908
650 4 $a Deadlines. $3 1473680
650 4 $a Finance. $3 559073
650 4 $a Statistics. $3 556824
655 7 $a Electronic books. $2 local $3 554714
690 $a 0770
690 $a 0508
690 $a 0510
690 $a 0463
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Universidade do Porto (Portugal). $3 1188642
773 0 $t Masters Abstracts International $g 85-11.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30984562 $z click for full text (PQDT)