Dynamic Factor Model Applications Based on Google Trends and Macroeconomic Data for Nowcasting the Gross Domestic Growth of Indonesia Restu Sri Rahayu(a), Irhamah(a*), Kartika Fithriasari(a)
a) Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Kampus ITS-Sukolilo, Surabaya, 60111, Indonesia
*irhamah[at]statistika.its.ac.id
Abstract
Dynamic Factor Model (DFM) is a development of time series analysis that is used to perform near-range nowcasting. This model can relate the monthly frequency of the predictor variable to the quarterly frequency of the response variable. DFM has been widely used in economic fields because it can relate economic variables observed in different periods. The availability of economic data, which tends to experience delays, is one of the problems that are commonly encountered. In general, formulating economic policies requires information on economic conditions that is readily available. Policymakers need to know the current economic conditions as a foundation or a basis for projecting future economic conditions. However, macroeconomic indicators tend to be released and available with a long delay. Furthermore,the economic condition of a country can be reflected in the GDP of country. The growth of GDP plays a very important role in helping policymakers and business society understand economic conditions of the country. GDP data has been delayed in its release for five weeks since the quarter ended. This condition occurs at the national levels. Therefore, this study aims to nowcast the growth of GDP in Indonesia using official statistics and google trends data using the DFM.
Keywords: Dynamic Factor Model (DFM), nowcasting, google trends
