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Nowcasting RDU with trends

Nowcasting RDU with trends. Based on Durham Paper By Ramy Khorshed. About Google Trends. Google Search query volume Y-axis search index X-axis time In 2008 , Google launched Google Insights for Search Revamped front-end in 2012.

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Nowcasting RDU with trends

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  1. Nowcasting RDU with trends Based on Durham Paper By Ramy Khorshed

  2. About Google Trends • Google Search query volume • Y-axis search index • X-axis time • In 2008, Google launched Google Insights for Search • Revamped front-end in 2012

  3. Google Trends: Example  Lax Scandal  Jane Goodall Primate Center Steve Jobs Speech

  4. Google Trends: Example

  5. Google Trends: Example

  6. Google Trends: Example

  7. Proof of Concept: • Etteredge (2005): • US unemployment rate • Cooper (2005): • Cancer • Polgreen(2008) and Ginsberg (2009): • Contagious diseases • Choi and Varian (2009): • Unemployment • Automobile demand • Vacation Destinations • Goel (2010): • Box-office revenue • First Month sales of video games • Rank of songs on the Billboard Hot 100

  8. Durham Paper Topic: • Can applying simple regression models enhanced by Google search volume data can improve the predictability of current and near-future economic conditions pertaining to Durham? • Specifically, I will adjust predictions of Raleigh-Durham International (RDU) passenger volume based on the number of queries related to RDU.

  9. Methodology • Model 0: log(yt) = α1 log(yt-1)+ α2log(yt-12)+et • Model 1: log(yt) = α1 log(yt-1)+ α2log(yt-12)+ α3xt +et • Data:

  10. Methodology • Model 0: log(yt) = α1 log(yt-1)+ α2log(yt-12)+et • Model 1: log(yt) = α1 log(yt-1)+ α2log(yt-12)+ α3xt +et • Trend Data:

  11. MAE = (1/T)Tt=1 |Pet| model 0 = 4.35% model 1 = 3.31% Improvement of 31.41% Results:

  12. Conclusions: • This result could help airport management better predict passenger volume allowing them to make better decisions and improve customer experience. • Durham hotels could look to more accurately anticipate demand for lodging and accordingly change price by incorporating search volumes into predictions based on past occupancy. • Durham real estate developers could incorporate monthly and daily query volumes for Durham to help determine real-estate value. • Raleigh-Durham searches from the search could be used to help guide marketing decisions.

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