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

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...

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Bibliographic Details
Published in:Journal of Electrical Systems and Information Technology 2023-12, Vol.10 (1), p.12-16, Article 12
Main Authors: Adu, Williams Kwasi, Appiahene, Peter, Afrifa, Stephen
Format: Article
Language:English
Subjects:
Accuracy
Autoregressive models
Coronaviruses
COVID-19
Cross correlation
Economic conditions
Employment
Engineering
Error reduction
Forecasting
Internet
Keywords
Labor force
Mechanical Engineering
Medical research
Queries
Review
Root-mean-square errors
Search engines
Trends
Unemployment
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Summary: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.
ISSN:2314-7172
2314-7172
DOI:10.1186/s43067-023-00078-1