Forecasting National and Regional Youth Unemployment in Spain Using Google Trends

Simionescu, Mihaela; Cifuentes‐Faura, Javier

doi:10.1007/s11205-022-02984-9

Cited by 9 publications

(5 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The highly accurate prediction of increasing short-term CUR can provide effective and timely suicide prevention interventions against some part of female suicides. Prediction of CUR via several types of panel-data analyses using online statistics including real-time data was reported to be more accurate and rapidly performed compared to traditional governmental prediction analysis based only on macroeconomic indicators published with a longer time lag [63,64]. Prevention of increasing CUR and timely implementation of support for the unemployed individuals based on rapid and highly accurate prediction of CUR can be expected to prevent an increase in the number of unemployed individuals at risk of suicide.…”

Section: Discussionmentioning

confidence: 99%

Impacts of Complete Unemployment Rates Disaggregated by Reason and Duration on Suicide Mortality from 2009–2022 in Japan

Matsumoto,

Motomura,

Okada

2023

Healthcare

View full text Add to dashboard Cite

In Japan, suicides had consistently decreased before the COVID-19 pandemic (from 2009–2019), but conversely increased after the pandemic outbreak (from 2020–2022). To identify the features of fluctuations of suicides in Japan, the standardized suicide mortality rates per 100,000 population (SMRP) disaggregated by gender (males/females) and age (10-year cohorts) from 2009–2022 were analyzed using interrupted time-series and joinpoint regression analyses. Temporal causalities from unemployment rate (CUR) disaggregated by unemployment duration and reasons for seeking work to SMRP were analyzed using vector autoregressive modelling with Granger causality analysis. SMRP fluctuations from 2009–2022 were composed of three patterns, such as positive discontinuity (increasing) synchronized with the pandemic outbreak, attenuations of decreasing trends before the pandemic, turning from decreasing before the pandemic to increasing/unchanging after the pandemic outbreak. Dismissal CUR positively related to SMRP of working-age generations, whereas voluntary CUR negatively related to SMRP of younger population (<30 years), which turned to persistently increasing before the pandemic (approximately 2016–2017). CUR shorter than 3 months positively related to SMRP of working-age females, which displayed promptly increasing synchronization with the pandemic outbreak. CUR longer than 12 months positively related to SMRP of working-age males, which contributed to persistently increasing SMRPs during the pandemic. These results suggest that increasing SMRP during 2020–2022 in Japan has been probably at-tributed to interactions among the pandemic-related factors, continuous vulnerabilities from before the pandemic and newly developing risk factors for suicides during the pandemic. Unexpectedly, increasing SMRPs of working-age males in 2022 suggest that either prolongation of the pandemic or the ending of the pandemic might positively affect suicides in Japan.

show abstract

Section: Discussionmentioning

confidence: 99%

Impacts of Complete Unemployment Rates Disaggregated by Reason and Duration on Suicide Mortality from 2009–2022 in Japan

Matsumoto,

Motomura,

Okada

2023

Healthcare

View full text Add to dashboard Cite

show abstract

“…Their main advantages are that they are free from revisions and can be obtained in specific geographical areas, such as the region of a country. However, the majority of studies on unemployment forecasting are conducted at the national level and there are very few at the regional level (see, for instance, Simionescu & Cifuentes-Faura, 2022).…”

Section: Introductionmentioning

confidence: 99%

Are national or regional surveys useful for nowcasting regional jobseekers? The case of the French region of Pays‐de‐la‐Loire

Cariou,

Charles,

Darné

2024

Journal of Forecasting

View full text Add to dashboard Cite

AbstactIn this paper we develop nowcasting models for the Pays‐de‐la‐Loire's jobseekers, a dynamic French regional economy. We ask whether these regional nowcasts are more accurate by only using the regional data or by combining the national and regional data. For this purpose, we use penalized regressions, random forest, and dynamic factor models as well as dimension reduction approaches. The best nowcasting performance is provided by the DFM estimated on the regional and regional‐national databases as well as the Elastic‐Net model with a prior screening step for which the national data are the most frequently selected data. For the latter, it appears that the Change in foreign orders in the industry sector, the OECD Composite leading indicator, and the BdF Business sentiment indicator are among the major predictors.

show abstract

“…This challenge is almost common in all countries, with Ghana not an exemption. This results in Policymakers making assessments in real-time using inadequate information, and knowing the present unemployment state which could help them better understand whether an economy is contracting or expanding and respond [ 18 ]. This paper tackles the case by using real-time Google trends data for prediction of unemployment claims in Ghana.…”

Section: Introductionmentioning

confidence: 99%

VAR, ARIMAX and ARIMA models for nowcasting unemployment rate in Ghana using Google trends

Adu

Appiahene

Afrifa

2023

Journal of Electrical Systems and Inf Technol

View full text Add to dashboard Cite

The analysis of the high volume of data spawned by web search engines on a daily basis allows scholars to scrutinize the relation between the user’s search preferences and impending facts. This study can be used in a variety of economics contexts. The purpose of this study is to determine whether it is possible to anticipate the unemployment rate by examining behavior. The method uses a cross-correlation technique to combine data from Google Trends with the World Bank's unemployment rate. The Autoregressive Integrated Moving Average (ARIMA), Autoregressive Integrated Moving Average with eXogenous variables (ARIMAX) and Vector Autoregression (VAR) models for unemployment rate prediction are fit using the analyzed data. The models were assessed with the various evaluation metrics of mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), median absolute error (MedAE), and maximum error (ME). The average outcome of the various evaluation metrics proved the significant performance of the models. The ARIMA (MSE = 0.26, RMSE = 0.38, MAE = 0.30, MAPE = 7.07, MedAE = 0.25, ME = 0.77), ARIMAX (MSE = 0.22, RMSE = 0.25, MAE = 0.29, MAPE = 6.94, MedAE = 0.25, ME = 0.75), and VAR (MSE = 0.09, RMSE = 0.09, MAE = 0.20, MAPE = 4.65, MedAE = 0.20, ME = 0.42) achieved significant error margins. The outcome demonstrates that Google Trends estimators improved error reduction across the board when compared to model without them.

show abstract

Forecasting National and Regional Youth Unemployment in Spain Using Google Trends

Cited by 9 publications

References 44 publications

Impacts of Complete Unemployment Rates Disaggregated by Reason and Duration on Suicide Mortality from 2009–2022 in Japan

Impacts of Complete Unemployment Rates Disaggregated by Reason and Duration on Suicide Mortality from 2009–2022 in Japan

Are national or regional surveys useful for nowcasting regional jobseekers? The case of the French region of Pays‐de‐la‐Loire

VAR, ARIMAX and ARIMA models for nowcasting unemployment rate in Ghana using Google trends

Contact Info

Product

Resources

About