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Observatorio de la industria Fintech no bancaria en España
No full textThis note presents the Non-Banking FinTech Industry Observatory in Spain, an experimental statistics developed to monitor the evolution of this rapidly growing sector. The Observatory, updated since 2020, is based on a census of FinTech companies, combining public and private sources to classify and analyse entities according to their activity, geographical distribution, and vertical type. Additionally, the Observatory provides financial information for this set of companies in terms of national accounting, distinguishing between financial and non-financial institutions, calculated based on their financial data. This note also includes a comparative analysis of the sector’s evolution between 2018 and 2023, highlighting a 50% increase in the number of entities with available financial statements, significant growth in assets and employment, and the consolidation of business models in verticals such as “Cryptoassets and Blockchain”.Esta nota presenta el Observatorio de la Industria Fintech No Bancaria en España, la estadística experimental desarrollada para monitorizar la evolución de este sector en constante crecimiento. El Observatorio, actualizado desde 2020, se elabora sobre la base de un censo de empresas fintech, en el que se combinan fuentes públicas y privadas para clasificar y analizar las entidades según su actividad, distribución geográfica y tipo de vertical en la que se encuentran. Además, el Observatorio presenta información en términos de la contabilidad nacional de este conjunto de empresas, desglosada entre las instituciones financieras y no financieras, calculada a partir de su información financiera. También se incluye en esta nota un análisis comparativo de la evolución del sector entre 2018 y 2023, en el que destacan un incremento del 50 % en el número de entidades para las que se dispone de estados financieros, un aumento significativo en los activos y en el empleo, y la consolidación de los modelos de negocio de verticales como la de «Criptoactivos y blockchain»
Some determinants of the post-pandemic weakness of household consumption
No full textRationale
Household consumption in Spain has shown considerable weakness following the pandemic and its recovery has been less marked than would have been expected based on income developments. This article characterises the types of households and the items in the consumption basket that have contributed most to this slackness.
Takeaways
•By consumption basket component, in 2023 average spending on durable goods (particularly cars) and semi-durable goods (in particular, clothing and footwear) posted the largest fall in real terms compared with 2019. Meanwhile, consumption linked to leisure and culture stood close to its pre-pandemic levels.
•By household type, the largest gaps in average household consumption compared to pre-pandemic levels are observed in high-income households and households where the reference person is between 35 and 54 or is a foreign national.
•Differences in consumption developments by type of household are related to heterogeneity across households in terms of both income developments and the composition of the consumption basket – specifically, the difference in the share of essential expenditure (food, rent, water and energy)
Growth Recurring in a Preindustrial Economy, 1277-2023 [dataset]
No full textDatos del PIB, PIB per cápita y distribución de la renta desde 1277 || GDP data, GDP per capita, and income distribution since 1277Índice de hojas - Índice series - Series - Notas || Sheet index - Series index - Time series - Note
Density forecast transformations
No full textLa decisión habitual de utilizar un esquema de pronóstico directo implica que las predicciones individuales ignoran la información sobre su dependencia entre horizontes. Sin embargo, esta dependencia es necesaria cuando el pronosticador debe construir, basándose en pronósticos de densidad directos, objetos predictivos que sean funciones de varios horizontes (por ejemplo, al construir tasas de crecimiento anual promedio a partir de tasas de crecimiento trimestral). Para abordar este problema proponemos usar cópulas, que permiten combinar las distribuciones predictivas individuales de h pasos hacia adelante en una distribución predictiva conjunta. Nuestro método resulta particularmente atractivo para aquellos profesionales para quienes cambiar la especificación de pronóstico directo es demasiado costoso. Mediante un estudio de Montecarlo, demostramos que nuestro enfoque proporciona una aproximación más precisa de la densidad verdadera que un enfoque que ignore la posible dependencia. Ofrecemos diversos ejemplos empíricos que evidencian el rendimiento superior de nuestro método y en los que construimos: i) pronósticos trimestrales utilizando pronósticos directos mes a mes, ii) pronósticos anuales promedio utilizando pronósticos directos mensuales año a año y iii) pronósticos anuales promedio utilizando pronósticos directos trimestre a trimestre.The common choice of using a direct forecasting scheme implies that the individual predictions ignore information on their cross-horizon dependence. However, this dependence is needed if the forecaster has to construct, based on direct density forecasts, predictive objects that are functions of several horizons (e.g. when constructing annual-average growth rates from quarter-on-quarter growth rates). To address this issue we propose using copulas to combine the individual h-step-ahead predictive distributions into one joint predictive distribution. Our method is particularly appealing to those for whom changing the direct forecasting specification is too costly. We use a Monte Carlo study to demonstrate that our approach leads to a better approximation of the true density than an approach that ignores the potential dependence. We show the superior performance of our method using several empirical examples, where we construct (i) quarterly forecasts using month-on-month direct forecasts, (ii) annual-average forecasts using monthly year-on-year direct forecasts, and (iii) annual-average forecasts using quarter-on-quarter direct forecasts
