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Econometric Computing in the Cloud

Explore the power of econometric computing in the cloud using R and machine learning techniques for causal inference, regression, and data visualization. Learn about cloud computing layers, services providers, and implement low-cost options using Amazon Web Services. Dive into real-world examples such as analyzing wine sales data in Vancouver, BC, for data exploration, visualization, and regression analysis.

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Econometric Computing in the Cloud

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  1. Econometric Computing in the Cloud Kuan-Pin Lin Department of Economics Portland State University

  2. Econometric Computing • Econometric Computing: Causal Inference of Economic Data for Decision Making • Econometrics and Predictive Analytics (Machine Learning, Data Mining) • Regression and Classification • Model Selection • Cross-Validation • Prediction Econ Comp in the Cloud

  3. Econometric Computing • Machine Learning from Econometrics • Causal Inference Methods • Confounding and Instrumental Variables • Regression Discontinuity • Difference in Difference • Advanced Topics • Non IID Data (Time Series, Panel Data) • High Dimensional Sparse Econometric Models Econ Comp in the Cloud

  4. Using R • Why R (and RStudio)? • Powerful, Popular, and Portable • Free (Nothing better than that!) • But, with steep learning curve! • Using R Packages • lm(), glm(), plm() for linear modeling • tree(), rpart() for machine learning • ggplot2() for data visualization • parallel() for multi-core and parallel computing Econ Comp in the Cloud

  5. Cloud Computing with R • Layers of Cloud Computing • Cloud Clients  SaaS, PaaS, laaS • IaaS: Infrastructure as a Service • PaaS: Platform as a Service • SaaS: Software as a Service • Cloud Services Providers • Microsoft Azure • Google App Engine • Amazon EC2, … Econ Comp in the Cloud

  6. Econometric Computing in the Cloud • A Low Cost Option • Amazon Web Services (AWS: Free Tier) • Elastic Compute Cloud (EC2) • Simple Storage Service (S3) • [($15 + 10c /gb) / month] after 1st year • RStudio Server Amazon Machine Image • Implementing R / Rstudio AMI • Guide for Dummies (are we?) Part II, Part III • Louis Aslett’s Guide Econ Comp in the Cloud

  7. An Example • Wine Sales in Vancouver BC • Total Weekly Sales of Imported and Domestic Table Wine in Vancouver, BC, Canadafrom week ending April 4, 2009 to week ending May 28, 2011 (372,228 sales) • Irregularly-spaced time series • Data Source: American Association of Wine Economists Econ Comp in the Cloud

  8. An Example • Wine Sales in Vancouver BC • 372,228 observations of 17 variables in an Excel spreadsheet: SKU #, Product Long Name, Store Category Major Name, Store Category Sub Name, Store Category Minor Name, Current Display Price, Bottled Location Code, Bottle Location Desc, Domestic/Import Indicator, VQA Indicator, Product Sweetness Code, Product Sweetness Desc, Alcohol Percent, Julian Week No, Week Ending Date, Total Weekly Selling Unit, Total Weekly Volume Litre Econ Comp in the Cloud

  9. An Example • Wine Sales in Vancouver BC • Data Exploration • What = Store Category Minor Name (Red/White) • Where = Store Category Sub Name (Countries) • Price = Current Display Price (Canadian $) • Quantity = Total Weekly Selling Unit (Bottles) • Data Visualization • Bar, Box, Point, Line, Histogram, Density • Data Analysis • Regression: Price Elasticity • Classification Econ Comp in the Cloud

  10. Let’s Go @ AWS!

  11. Let’s Go @ Digital Ocean

  12. Let’s Go @ WISE!(Login required)

  13. Thank You! See You in the Cloud

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