Combining official and Google Trends data to forecast the Italian youth unemployment rate

Naccarato, Alessia; Falorsi, Stefano; Loriga, Silvia; Pierini, Andrea

doi:10.1016/j.techfore.2017.11.022

Cited by 82 publications

(71 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The earliest attempts to forecast unemployment rate with search query reveal the predictive power of query data for labor market [13][14][15] and many studies follow (e.g. [16][17][18]). While most of the papers utilize ARIMA-type models, Onorante and Koop [19] apply Dynamic Model Selection/Averaging and Scott and Varian [20] develop the Bayesian structural time series model.…”

Section: Related Workmentioning

confidence: 99%

Nowcasting Unemployment Rates with Smartphone GPS Data

Moriwaki

2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Unemployment rate is one of the most important macroeconomic indicators. Central governments and market participants heavily rely on the index to assess the economies. However, official statistics of unemployment rate are released infrequently with substantial delay. Prediction of official statistics of labor market will be helpful for these authorities as well as private companies and even workers. In this paper, we combine massive location data coming from smartphones and mixed data sampling (MIDAS) techniques to predict current unemployment rate in Japan. We found GPS data is very useful to predict the status of labor markets.

show abstract

Section: Related Workmentioning

confidence: 99%

Nowcasting Unemployment Rates with Smartphone GPS Data

Moriwaki

2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…The studies in the literature on this topic did not establish if the traditional data sources could completely be replaced by Internet data or if a combination of them is better. Some authors obtained better results when combining the data in their models [36][37][38]. In our study, we used econometric models to explain the unemployment rate only using official data and Internet data.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This keyword was employed by Francesco (2009) to prove that the forecasts based on models using Google search data outperformed other types of forecasts for the Italian unemployment rate [44]. Moreover, Naccarato et al (2015) and Naccarato et al (2018) also used this keyword [37,38]. Naccarato et al (2015) connected this keyword to the official unemployment rate from the labor force survey, discovering a cointegration relationship between these variables [37].…”

Section: Literature Reviewmentioning

confidence: 99%

What Does Google Trends Tell Us about the Impact of Brexit on the Unemployment Rate in the UK?

2020

View full text Add to dashboard Cite

Considering the debate related to the potential effects of Brexit on the UK economy, the aim of this paper is to assess the impact of Brexit on the monthly unemployment rate since the vote for the UK leave from the European Union. This is one of the most important indicators of sustainable development for the country. The novelty of this research is given by the use of microdata to reflect the political instability brought by Brexit, with Google Trends being the tool for collecting the data. Moreover, the data for the four countries that compose the UK are considered (England, Northern Ireland, Scotland, Wales) in a panel data and multilevel framework. The results are consistent with the analysis of important macroeconomic indicators and indicate that Brexit concerns decreased the unemployment rate in the period June 2016–March 2019, with few arguments being provided for this. The state policies should encourage the investment in order to support the future economic growth and sustainable development of the UK.

show abstract

“…For example, Choi and Varian (2012) show that the categories 'trucks 40 & SUVs' and 'automotive insurance' help predict motor vehicle sales, while D' Amuri and Marcucci (2017) show that the 'jobs' category helps forecast US unemployment. Similarly, Naccarato et al (2018) use the frequency of the search term 'job offers' to forecast Italian youth unemployment, and Yu et al (2018) use the search terms 'oil consumption', 'oil inventory' and 'oil price' to 45 predict (changes in) oil consumption. Arguably, all these out-of-sample studies use somewhat simpler (autoregressive) models than Scott and Varian's (2014a; 2014b) BSTS model.…”

Section: Introductionmentioning

confidence: 99%

Can Google Search Data Help Predict Macroeconomic Series?

et al. 2018

View full text Add to dashboard Cite

We use Google search data with the aim of predicting unemployment, CPI and consumer confidence for the US, UK, Canada, Germany and Japan. Google search queries have previously proven valuable in predicting macroeconomic variables in an in-sample context. To our knowledge, the more challenging question of whether such data have out-of-sample predictive value has not yet been satisfactorily answered. We focus on out-of-sample nowcasting, and extend the Bayesian Structural Time Series model using the Hamiltonian sampler for variable selection. We find that the search data retain their value in an outof-sample predictive context for unemployment, but not for CPI and consumer confidence. It may be that online search behaviour is a relatively reliable gauge of an individual's personal situation (employment status), but less reliable when it comes to variables that are unknown to the individual (CPI) or too general to be linked to specific search terms (consumer confidence).

show abstract

Combining official and Google Trends data to forecast the Italian youth unemployment rate

Cited by 82 publications

References 25 publications

Nowcasting Unemployment Rates with Smartphone GPS Data

Nowcasting Unemployment Rates with Smartphone GPS Data

What Does Google Trends Tell Us about the Impact of Brexit on the Unemployment Rate in the UK?

Can Google Search Data Help Predict Macroeconomic Series?

Contact Info

Product

Resources

About