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F rom D ata to Information Principles behind Smart Data

F rom D ata to Information Principles behind Smart Data. Roberto Rigobon MIT Sloan, NBER, CSAC. Big Distance!. Big Distance!. The world is not lacking of Data. Lacking of Careful Empirics. Lacking of Managerial Data Analysis. Typical mistakes!. “Let the data speak for itself”

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F rom D ata to Information Principles behind Smart Data

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  1. From Data to InformationPrinciples behind Smart Data Roberto Rigobon MIT Sloan, NBER, CSAC

  2. Big Distance!

  3. Big Distance! The world is not lacking of Data Lacking of Careful Empirics Lacking of Managerial Data Analysis

  4. Typical mistakes! • “Let the data speak for itself” • This is impossible. • The data speaks through the lenses we use to collect it and interpret it. • Model selection is not equivalent to picking the best model after running 3000 regressions • We need robust strategies to take into account our ignorance

  5. Typical Mistakes • “Great Data is Great Information” • New technologies reduces the cost of collecting data, they do not improve the quality of data analytics. • Word Processors • Reduce the cost of writing words and pages in comparison to 300 years ago • But it has not improved the quality of literature!

  6. Data Types

  7. Economic Indicators

  8. Economic Indicators

  9. Economic Indicators

  10. What is Big Data? • Collected through non-standard procedures • Computational challenges • Econometric challenges

  11. What is Big Data? • Collected through non-standard procedures • Computational challenges • Econometric challenges • Defining it through its characteristics: • Volume • Velocity • Variety • Problems: • Are the varieties, volume, and velocity representative? • When do these characteristics matter?

  12. Data Types

  13. New Home Sales

  14. New Home Sales Open Houses and Phones

  15. New Home Sales Open Houses and Phones Contract Realtor

  16. New Home Sales Open Houses and Phones Contract Realtor Search for Mortgage

  17. Inflation and Price Indexes • Alberto Cavallo

  18. Online Information and Indexes Our Approach to Daily Inflation Statistics 1 2 3 4 5 Use scraping technology Connect to thousands of online retailers every day Find individual items Store and process key item information in a database Develop daily inflation statistics for ~20 countries • Date • Item • Price • Description

  19. Countries covered

  20. Argentina (http://www.inflacionverdadera.com)

  21. USA (http://bpp.mit.edu/usa/)

  22. Scarcity

  23. Trade

  24. Trade

  25. Trade

  26. Trade

  27. Smart Data

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