Web scraping based online consumer price index: The “IPC Online” case

Uriarte, Juan I.; Toro, Gonzalo R. Ramírez Muñoz de; Larrosa, Juan Manuel Ceferino

doi:10.3233/jem-190464

Cited by 9 publications

(5 citation statements)

References 15 publications

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“…Mustapa et al (2019) evaluated the dependability of online data prices to forecast the inflation of vegetables and fish in Malaysia with promising results. Uriarte et al (2019) implemented a web scraping technique for monitoring prices in a mid-urban area in Argentina and found that web scraping combined with big data techniques enabled estimation of more individualized and efficient metrics, whose quality was comparable to official statistics. Aparicio and Cavallo (2021) and Cavallo and Rigobon (2016) confirmed this result and stated that online-based price indexes were comparable to the traditional CPI despite methodological differences in multiple countries.…”

Section: Literature Reviewmentioning

confidence: 99%

Measuring food inflation during the COVID-19 pandemic in real time using online data: a case study of Poland

Jaworski

2021

BFJ

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PurposeThe purpose of this study paper is to focus on developing novel ways to monitor an economy in real time during the COVID-19 pandemic. A fully automated framework is proposed for collecting and analyzing online food prices in Poland. This is important, as the COVID-19 outbreak in Europe in 2020 has led many governments to impose lockdowns that have prevented manual price data collection from food outlets. The study primarily addresses whether food price inflation can be accurately measured during the pandemic using only a laptop and Internet connection, without needing to rely on official statistics.Design/methodology/approachThe big data approach was adopted to track food price inflation in Poland. Using the web-scraping technique, daily price information about individual food and non-alcoholic beverage products sold in online stores was gathered.FindingsBased on raw online data, reliable estimates of monthly and annual food inflation were provided about 30 days before final official indexes were published.Originality/valueThis is the first paper to focus on measuring inflation in real time during the COVID-19 pandemic. Monthly and annual food price inflation are estimated in real time and updated daily, thereby improving previous forecasting solutions with weekly or monthly indicators. Using daily frequency price data deepens understanding of price developments and enables more timely detection of inflation trends, both of which are useful for policymakers and market participants. This study also provides a review of crucial issues regarding inflation that emerged during the COVID-19 pandemic.

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Section: Literature Reviewmentioning

confidence: 99%

Measuring food inflation during the COVID-19 pandemic in real time using online data: a case study of Poland

Jaworski

2021

BFJ

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“…The focus of the research is on dynamic pricing, defined as the weekly rate of price change. Data come from a previous project Uriarte et al (2019), and they were web scraped from December 2015 to February 2021 from diverse large retailers in Argentina. Data was gathered on 766 wine products on a weekly basis, where a week is defined from 1st-7th, 8th-14th, 15th-21st and 22nd-28th day windows of each month.…”

Section: Methodology and Estimationsmentioning

confidence: 99%

A dynamic analysis of wine pricing in Argentina

Larrosa

Gutiérrez

Toro

et al. 2022

IJWBR

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Purpose The purpose of the study is to investigate micro determinants for dynamic wine pricing in Argentina. We test whether attributes and time affect the price rate of change. The rate of change is selected given the inflationary context of the country. The analysis provides valuable information for wine marketing decisions. Design/methodology/approach The modeling approach relies on panel data analysis for exploiting the data cross-section and time dimension. The contribution explores a massive price dataset at a weekly frequency. The dependent variable is the weekly price variation rate for product/wine and covariates are attributes, time and nominal variables. Given that endogeneity issues arose, the estimations rely on a two-stage least squares and instrumental variables with cluster-robust errors. Findings Estimations show that attributes, time and cost variables are statistically significant, with clear seasonal patterns and quality segmentation affecting pricing: wines made out of specific grapes such as Chenin, Merlot and Seedling or composing a broad category such as red wine, exhibit price undershooting (price rate of change below average). On the other hand, wines out of grapes such as Bonarda, Margaux, Mistela, Moscatel, Oporto, Tannat and Sauvignon Blanc show price overshooting (rate of change above average). In summary, wine made from determined grapes and specific wineries show divergent pricing. Research limitations/implications Covariates such as alcohol content, label descriptor information, winery history, substitute competition and vintage, among others, have not been considered given that the research analyzes more than 750 wine products. Another limitation is that the work does not explore many time-series covariates, such as promotions and idiosyncratic shocks. Practical implications The contribution presents new information on wine pricing patterns affected by weeks, months and years, including the effect of the prolonged 2020 Argentine lockdown. It also analyzes estimations on pricing at the level of grape/blend and wineries previously unknown in this market. The information can influence inventory decisions on the side of the sellers and purchase decisions on the side of consumers. Social implications The analysis includes fine but also low-cost wines that form part of the diet of low-income families in the country. The work detects a divergent pattern in pricing divided by the quality/price of the wine. It also presents information on price timing that may help consumers in the best moment to buy. Originality/value The contribution analyzes unprecedented information on weekly wine prices and presents evidence of pricing tactics from a point-of-sale perspective: It identifies different adjustment speeds related to product features and time effects.

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“…La tecnología y los procesos que lo permiten se han explorado ampliamente en el campo de la ciencia de datos, donde los investigadores aplican esta información a múltiples dominios de contenido (Landers et al, 2016); según lo descrito por Marres & Weltevrede (2013), en el periodismo, el raspado web se ha utilizado para evaluar la importancia de las noticias internacionales contando la cantidad de veces que los usuarios de las redes sociales mencionaron esas historias, Kiran & Mownika (2021) indica que el web Scraping es el procedimiento que se utiliza comúnmente en la minería de datos, Januzaj et al, (2019) utilizaron para comparar la demanda del mercado y los planes de estudio universitarios, Ullah et al, (2018) presentaron un estudio para extraer tendencias y sugerir el mejor precio de un producto objetivo, Muehlethaler & Albert, (2021) utilizo en la industria textil y afirma que en menos de 24 horas lograron extraer 68 campos basados en texto que describen un total de 24,701 prendas que le ayuda a realizar estimaciones, Almeida de Oliveira & Arantes Baracho Porto (2016) utilizo para analizar información relevante sobre las atracciones turísticas de Minas Gerais, Landers et al (2016) lo utilizo en el área de la psicología para encontrar patrones de comportamiento humano, Muñoz Mandujano et al (2018), lo empleo para almacenar datos climatológicos y Hernández et al (2015), lo utilizo para realizar análisis político en las redes sociales. Por ello web Scraping es una técnica que facilita la extracción de información de forma masiva y automatizada, para que pueda ser usada de acuerdo a las necesidades del usuario (Huaman Hilari & Quispe Ramos, 2019;Dewi et al, 2019) por ello es una herramienta de empoderamiento (Uriarte et al, 2020).…”

Section: Pág 10515unclassified

Indexación de Sitios Web para Optimizar la Búsqueda de Paquetes Turísticos Basado en Web Scraping

Cauna-Huanca,

Condori Chávez,

Quispe Caira

et al. 2023

Ciencia Latina

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La técnica del Web Scraping permite la extracción de contenido de varios sitios web, recabando información de interés para el usuario. El presente proyecto tiene como finalidad desarrollar un sitio web en la cual pueda almacenar información de los diferentes paquetes turísticos que son ofertados por las agencias de viaje que operan en la región de Puno utilizando la técnica del web Scraping. La población está conformada por 38 páginas web según inscritas en IPERÚ Puno. Para la elaboración del algoritmo de extracción se utilizó la metodología de desarrollo de software XP y para el contaste de la hipótesis se utilizó prueba de rangos con signo de Wilcoxon. Como resultado, el análisis de la estructura DOM permitió el desarrollo del algoritmo de extracción, haciendo uso de Python como lenguaje de programación, también se puso a prueba la eficiencia del algoritmo, el cual demostró ser eficiente en comparación con la el programa webscraper. Se determinó que la complejidad algorítmica es lineal. Del desempeño de nuestro sitio web según la puntuación global de PageSpeed Insights está en la categoría rápida (97 puntos). La evaluación del sitio web basado en la norma ISO 25000 proporcionó una valoración de 6.67/10 puntos como calidad total, considerado como nivel aceptable y grado satisfactorio. Se concluye que la implementación del sitio web facilita la búsqueda de diferentes paquetes turísticos.

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Web scraping based online consumer price index: The “IPC Online” case

Cited by 9 publications

References 15 publications

Measuring food inflation during the COVID-19 pandemic in real time using online data: a case study of Poland

Measuring food inflation during the COVID-19 pandemic in real time using online data: a case study of Poland

A dynamic analysis of wine pricing in Argentina

Indexación de Sitios Web para Optimizar la Búsqueda de Paquetes Turísticos Basado en Web Scraping

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