The Effects of Temporal Aggregation on Search Engine Data
Heather L.R. Tierney,
Jiyoon Kim () and
Zafar Nazarov ()
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Heather L.R. Tierney: Economics Department, Doermer School of Business, Neff 340B Purdue University, Fort Wayne, 2101 East Coliseum Boulevard, Fort Wayne, IN, 46805, U.S.A.
Jiyoon Kim: Economics Department, Doermer School of Business, Neff 340J Purdue University, Fort Wayne, 2101 East Coliseum Boulevard, Fort Wayne, IN, 46805, U.S.A.
Zafar Nazarov: Economics Department, Doermer School of Business, Neff 340A Purdue University, Fort Wayne, 2101 East Coliseum Boulevard, Fort Wayne, IN, 46805, U.S.A.
Review of Economics & Finance, 2018, vol. 12, 57-71
Through the use of structured machine learning, this paper examines the effects of temporal aggregation on big data from Google Analytics and Google Trends. Google Analytics is used to obtain daily and weekly tourism data from the Charleston Area Convention and Visitors Bureau (CACVB) website, and Google Trends is used to obtain an index formed from big data of weekly, monthly, and quarterly data for seven economic variables. Taking into account the different levels of aggregation, the CDFs and the estimated structured machine learning output are used to study the effects of temporal aggregation. The Kolmogorov-Smirnov test rejects the null of equivalent data distributions in the vast majority of cases for the CACVB data, but this is not the case for the economic variables. Through data mining techniques, this paper also finds that the level of aggregation has the potential of affecting the level of integration and the estimated structured machine learning output for both the CACVB data and the seven economic variables.
Keywords: Big data; Machine learning; Data mining; Aggregation; Unit roots; Scaling effects; Normalization effects (search for similar items in EconPapers)
JEL-codes: C55 C43 C19 (search for similar items in EconPapers)
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