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

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(y t ) = α 1 log(y t-1 )+ α 2 log(y t-12 )+e t  Model 1: log(y t ) = α 1 log(y t-1 )+ α 2 log(y t-12 )+ α 3 x t +e t  Data:

10. Methodology  Model 0: log(y t ) = α 1 log(y t-1 )+ α 2 log(y t-12 )+e t  Model 1: log(y t ) = α 1 log(y t-1 )+ α 2 log(y t-12 )+ α 3 x t +e t  Trend Data:

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

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.