Case Study 2.1: Predicting Wage I Instructor: Victor Chernuzkov Activity Type: Optional Case Study Description: Predict wages using various characteristics of workers and assess predictive performance. Why this Case Study? Prediction is getting important these days in the age of big data. Participants can apply a simple model from this class and assess the prediction performance of their model. Self-Help Package Contents: The video that covers this case study is given in Module 2, Segment 1.4. Self-Help-Package.zip Codebook.rtf contains the description of worker job-relevant characteristics. pay.discrimination.Rdata: the CPS (2012) data on wages and job-relevant worker characteristics, such as experience, gender, education. Regression1.4.CaseStudy.R predicts expected wage given worker characteristics using linear model with linear and quadratic specifications. In addition, it evaluates the performance of the predictor by: r.squared and mean squared error, with and without sample splitting.Regression.1.4.pdf is the set of slides describing the wage prediction model. .Rhistory Time Required: The time required to do this activity varies depending on your experience in the required programming background. We suggest planning somewhere between 1 & 3 hours. Remember, this is an optional activity for participants looking for hands-on experience.
Case Study 2.1: Predicting Wage I Instructor: Victor Chernuzkov Activity Type: Optional Case Study Description: Predict wages using various characteristics of workers and assess predictive performance. Why this Case Study? Prediction is getting important these days in the age of big data. Participants can apply a simple model from this class and assess the prediction performance of their model. Self-Help Package Contents: The video that covers this case study is given in Module 2, Segment 1.4. Self-Help-Package.zip Codebook.rtf contains the description of worker job-relevant characteristics. pay.discrimination.Rdata: the CPS (2012) data on wages and job-relevant worker characteristics, such as experience, gender, education. Regression1.4.CaseStudy.R predicts expected wage given worker characteristics using linear model with linear and quadratic specifications. In addition, it evaluates the performance of the predictor by: r.squared and mean squared error, with and without sample splitting.Regression.1.4.pdf is the set of slides describing the wage prediction model. .Rhistory Time Required: The time required to do this activity varies depending on your experience in the required programming background. We suggest planning somewhere between 1 & 3 hours. Remember, this is an optional activity for participants looking for hands-on experience.