Can internet searches forecast tourism inflows?

Artola, Concha; Pinto, Fernando; García, Pablo de Pedraza

doi:10.1108/ijm-12-2014-0259

Cited by 70 publications

(45 citation statements)

References 11 publications

(9 reference statements)

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“…This transformation allows a comparison of aggregates from the web data with numbers inferred from the NSO data. This is in line with other curated synopses of huge data sets that have been commonly used to make predictions in the field of "nowcasting" (Choi and Variant, 2012;Askitas and Zimmermann, 2009;Artola and Galan, 2012;Antenucci et al, 2014;Artola et al, 2015). In terms of Tambe's microscope analogy (Taylor et al, 2014), we are not tapping into granularity to look at the labor demand organism at a new level of detail.…”

Section: Data Generation Processessupporting

confidence: 63%

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Survey vs Scraped Data: Comparing Time Series Properties of Web and Survey Vacancy Data

Pedraza

Visintin

Tijdens

et al. 2019

IZA Journal of Labor Economics

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This paper studies the relationship between a vacancy population obtained from web crawling and vacancies in the economy inferred by a National Statistics Office (NSO) using a traditional method. We compare the time series properties of samples obtained between 2007 and 2014 by Statistics Netherlands and by a web scraping company. We find that the web and NSO vacancy data present similar time series properties, suggesting that both time series are generated by the same underlying phenomenon: the real number of new vacancies in the economy. We conclude that, in our case study, web-sourced data are able to capture aggregate economic activity in the labor market.

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Section: Data Generation Processessupporting

confidence: 63%

“…In economics research, labor markets are among the areas in which Big Data is increasingly being used (Choi and Variant, 2012;Askitas and Zimmermann, 2009;Artola and Galan, 2012;Artola et al, 2015;Antenucci et al, 2014;Kureková et al, 2015;Lenaerts et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Survey vs Scraped Data: Comparing Time Series Properties of Web and Survey Vacancy Data

Pedraza

Visintin

Tijdens

et al. 2019

IZA Journal of Labor Economics

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“…represents the large literature on using Internet data for forecasting purposes (Artola, Pinto and de Pedraza, 2015). Employing Google activity data, the authors demonstrate that traditional time-series forecasting models for tourism inflows into Spain can be improved using Google activity measures.…”

Section: This Special Issuementioning

confidence: 99%

The internet as a data source for advancement in social sciences

Askitas

Zimmermann

2015

International Journal of Manpower

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. The Institute for the Study of Labor (IZA) in Bonn is a local and virtual international research center and a place of communication between science, politics and business. IZA is an independent nonprofit organization supported by Deutsche Post Foundation. The center is associated with the University of Bonn and offers a stimulating research environment through its international network, workshops and conferences, data service, project support, research visits and doctoral program. IZA engages in (i) original and internationally competitive research in all fields of labor economics, (ii) development of policy concepts, and (iii) dissemination of research results and concepts to the interested public. Terms of use: Documents inIZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be available directly from the author. This paper advocates the use of Internet data for social sciences with a special focus on human resources issues. It discusses the potentials and challenges of Internet data for social sciences and presents a selection of the relevant literature to establish the wide spectrum of topics, which can be reached. Such data represent a large and increasing part of everyday life, which cannot be measured otherwise. They are timely, perhaps even daily following the factual process, they typically involve large numbers of observations, and they allow for flexible conceptual forms and experimental settings. Internet data can successfully be applied to a very wide range of human resource issues including forecasting (e.g. of unemployment, consumption goods, tourism, festival winners and the like), nowcasting (obtaining relevant information much earlier than through traditional data collection techniques), detecting health issues and well-being (e.g. flu, malaise and ill-being during economic crises), documenting the matching process in various parts of individual life (e.g. jobs, partnership, shopping), and measuring complex processes where traditional data have known deficits (e.g. international migration, collective bargaining agreements in developing countries). Major problems in data analysis are still unsolved and more research on data reliability is needed. Current research is highly original but also exploratory and premature. Our article reviews the current attempts in the literature to incorporate Internet d...

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“…Prediction of economic activities by using social network data or internet search data ahead actual activities has been reported in the stock market, marketing and tourism [5][6][7]. Recently, prediction of economic activities by using social network data or internet search data have been reported in agriculture [4,[8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Forecasts of the Amount Purchase Pork Meat by Using Structured and Unstructured Big Data

et al. 2020

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It is believed that the huge amount of information delivered to the consumers through mass media, including television and social networks, may affect consumers’ behavior. The purpose of this study was to forecast the amount required to purchase pork belly meat by using unstructured data such as broadcast news, TV programs/shows and social network as well as structured data such as consumer panel data, retail and wholesale prices and production outputs in order to prove that mass media data release can occur ahead of actual economic activities and consumer behavior can be predicted by using these data. By using structured and unstructured data from 2010 to 2016 and five forecasting algorithms (autoregressive exogenous model and vector error correction model for time series, gradient boosting and random forest for machine learning, and long short-term memory for recurrent neural network), the amounts required to purchase pork belly meat in 2017 were forecasted and compared with the actual amounts to validate model accuracy. Our findings suggest that when unstructured data were combined with structured data, the forecast pattern is improved. To date, our study is the first report that forecasts the demand of pork meat by using structured and unstructured data.

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Can internet searches forecast tourism inflows?

Cited by 70 publications

References 11 publications

Survey vs Scraped Data: Comparing Time Series Properties of Web and Survey Vacancy Data

Survey vs Scraped Data: Comparing Time Series Properties of Web and Survey Vacancy Data

The internet as a data source for advancement in social sciences

Forecasts of the Amount Purchase Pork Meat by Using Structured and Unstructured Big Data

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