Google Searches as a Means of Improving the Nowcasts of Key Macroeconomic Variables

Abstract

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Monitoring Influenza Epidemics in China with Search Query from Baidu

“…Ginsberg et al [18] only selected 45 significant keywords from 50 million. The method of exhaustion employed by Ginsberg et al [18] is computationally expensive and not easily reproducible by researchers with limited resources [27]. In some cases, researchers have solely relied on keywords recommended by Google [23], [24], [26].…”

“…In the same year as the Ginsberg's publication [18], several studies investigated the usefulness of Google searches for forecasting unemployment in various countries [21±25]. Several papers also used search query data to predict consumption [26], [27], house pricing and sales [28], and travel and consumer confidence [27]. Though studies using web search query data have achieved good results in empirical practice, the field is still young and rapidly developing, with room for discussion and improvement.…”

A MIDAS modelling framework for Chinese inflation index forecast incorporating Google search data

“…Wu and Brynjolfsson (2013) used real estate related search series to predict housing prices and sales, and demonstrated Google search data predicted future business activities accurately. Many other researches (Askitas and Zimmermann 2009;Kholodilin et al 2009;Goel et al 2010;McLaren 2011;Guzman 2011;Schmidt 2011, 2012;Zhu et al 2012;Chen and Chen 2010) used the search data to forecast many economic indicators, like unemployment rate, inflation expectations, private consumption, consumer behavior, home sales and business cycle. For the forecasting of financial markets, Da et al (2011) constructed a new index to measure investor's attention using search frequency of Google, and forecasted the stock prices in the next weeks.…”

Health and well-being in the great recession

“…The data has been used for consumption research (Kholodilin et al, 2010;Vosen, 2009, 2010), housing prices (Kulkarni et al, 2009), unemployment (Askitas andZimmermann, 2009;D'Amuri and Marcucci, 2010), as well as finance (Sims, 2010) and policy (Bersier, 2010). It has also been used to enhance the performance of more traditional forecasting models as in Kholodilin et al (2009). Our own exercise on short-term forecasting of German unemployment (Askitas and Zimmermann, 2009) served us well in predicting a quiet labor market at a time when most experts thought it was highly unlikely.…”