Utilizing Google Trends Data to Examine the Impact of Unemployment Rates on Indonesia's Gross Domestic Product - Jane, Giani Jovita; Hasabi, Rafif; Purnatadya, Sinatrya Dwi; Kartiasih, Fitri (Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer, 2024)
Abstract Data related to the economy have varying frequencies and have delays in publication time. Such as data on the Open Unemployment Rate (OUR) with a semi-annual frequency and Gross Domestic Product at Constant Prices (riil GDP) according to expenditure with a quarterly frequency. So, frequency conversion is required to conduct simple regression modelling using these data. On the other hand, big data such as Google Trends is an additional predictor to estimate OUR and GDP data to overcome delays in publication time. Then the estimated data is modelled to investigate the effect of OUR on GDP. Data conversion uses the Chow-Lin method, while estimation with Google Trends data uses robust regression. The study shows that the estimation results using Google Trends as an additional predictor provide more accurate results than without Google Trends data for OUR and GDP data. Based on the robust regression results, it can be concluded that the OUR has a negative and significant effect on GDP. The findings provide valuable insights for supporting sustainable economic policy and further research on economic analysis.