The statistics show the level and daily evolution of sales made by companies included in the Immediate Supply of Information (SII) system.The daily sales series comes from the VAT management system based on the SII, implemented since July 2017 (R.D. 596/2016, of 2 December).This system allows tax information to be exchanged practically in real time between the Tax Agency and taxpayers obliged to use the SII by sending the details of invoicing records within four days via the Tax Agency's eOffice.
Daily data in this publication can be considered an early indicator of the sales published in the monthly Sales, Employment and Wages in Large Companies and the quarterly Sales, Employment and Wages in Large Companies and SMEs reports.Both provide information from VAT and withholdings from work income tax returns;the former includes only taxpayers considered as Large Companies for tax purposes, while the latter adds information on SMEs in the form of public limited companies and limited liability companies.In addition to complementing these two publications, the rapid availability and wide coverage of the information is an added value for short-term forecasting and monitoring of both tax collection and macroeconomic variables.
Those required to file their tax returns through the SII are Large Companies (those with a turnover the previous year of more than 6 million euros), VAT groups and companies signed up to the Monthly Refund Register (REDEME).Given the size and profile of these taxpayers, the daily sales available account for around 70% of total domestic sales of all VAT taxpayers (the percentage is higher if the total declared volume of transactions is considered), with a great diversity of coverage by activity (SII businesses are highly representative of some sectors, while in others their number and weight are small, as is the case in part of construction, in hotels and restaurants or in some types of transport).
The geographical scope is the so-called Common Taxation Area;That is, companies operating exclusively in territories managed by the tax authorities of the Basque Country and Navarre, as well as those operating in territories outside the scope of VAT application (the Canary Islands, Ceuta and Melilla) are excluded.
Taxpayers are territorially assigned to the place where they have their tax address.From the SII system it is possible to obtain information on the tax address of the seller and buyer, but it is not possible to determine unequivocally the place where the sales have taken place.
Companies included in the Immediate Supply of Information (SII) system are classified according to the activity declared by the companies themselves.Activities are classified according to the headings of the Economic Activities Tax (IAE).This is regulated by Royal Legislative Decree 1175/1990, approving the rates and instruction of the Economic Activities Tax, and its successive updates.For the purposes of publishing the report, companies are classified with four digits of the CNAE-2009 and grouped based on this classification.
The variable presented in these statistics is obtained by aggregating non-exempt taxable bases available in the register of invoices issued.This tax base comprises both the part corresponding to the sale of goods and the provision of services and is assimilable to the concept of domestic sales (excluding exempted sales) available in the Sales, Employment and Wages statistics in tax returns.
Economic series with a daily frequency pose a series of problems that are not so pronounced in lower frequency series (monthly or quarterly).These problems stem mainly from the complex and unstable structure of the calendar (different length and composition of months, mobile seasonal elements such as Easter, leap years and a mobile working calendar which also interacts with the weekly composition of months) and the existence of seasonal elements overlapping cycles of different frequencies.
To this we must add the intensity that its irregular component usually presents (lessened in the case of monthly data) and the impact of exogenous elements that distort the usual behaviour of the systematic components of these series (in the case of the SII, these elements may be, for example, different invoicing dates in the different companies, which cause abnormally high values that interact with other components of the series).
For all these reasons, the estimation of a robust business cycle signal is particularly difficult and requires processing based on econometric models that represent the aforementioned phenomena in a joint and statistically satisfactory way.For this purpose, a structural time series model is used which flexibly takes all these elements into account and which includes, in order to preliminarily correct part of these effects, processing deterministic exogenous variables.The latter variables are designed to control the presence of anomalous observations and calendar-specific effects which, due to their aperiodic nature or mobile periodicity, do not fit into the structural representation considered.
The estimation of a robust business cycle signal in daily series is particularly difficult and an approach based on structural econometric time series models is used.In this case, the TBATS modelling approach1,2, which is based on representing unobserved components (trend, seasonality, irregularity) by means of explicit dynamic models, is applied to the series adjusted for deterministic effects.This approach is very flexible as it allows several seasonal components (weekly, monthly and annual) to be considered simultaneously and, thanks to its trigonometric representation, fractional periodicities (e.g. those induced by leap years) can be processed, offering a very compact representation of these components.From this modelling it is possible to obtain the trend series and the series adjusted for seasonal and calendar variations (CVEC), as well as the corresponding seasonal factors in their different frequencies (weekly, monthly and annual).The average of these factors is zero at each of these frequencies.
1 TBATS:Trigonometric seasonality, Box-Cox transformation, ARMA innovations, Trend and Seasonality (De Livera et al., 2011).
2 A specific application to these series can be found in Cuevas, Ledo y Quilis, SERIEs, 2021, 10.1007/s13209-021-00251-7.https://link.springer.com/content/pdf/10.1007/s13209-021-00251-7.pdf