I bought this book in hard copy around 3 weeks. I felt really happy when I finished the book. The last time I finished a book in hard copy might be 4-5 years ago.
The interest of the book comes from my phone interview with Discover Financial in Nov 2014. I didn't handle the questions well on assumptions of regression. Then I realized I have produced so many "accurate" financial reports in my daily work and almost lose the healthy suspicion on the model. I think this book does answer part of the question.
This books covers different topics of forecast and explains the common issues in some of the forecast. I spend some time to summary them.
In the next blogs, I will examine the forecast I've done in my work or life, and find why they fail and if possible find a way to improve it in the future.
| Forecast | Existing forecast | Why Fail (Challenging) | Why Succeed | How to improve |
| Recession | Most economists failed | 1. Fail to forecast house bubble - Limit data in US history 2. Fail to forecast impact of house bubble to whole economics - Limit data in US history - Large leverage 3. Rating agency provided wrong rating from their incentives - Wrong assumption of independant events | ||
| Political forecast | TV show forecast bad Some tools are good | 1. Bias because of party position | ||
| Baseball | Some tools are good | 1. Difficult to bring quality attributes into model | 1. Lots of input data 2. Only a few output variable | |
| Weather | Some tools are good | 1. Nonlinear behavior 2. Simplification of linear requires lots of calculation | 1. Lots of input data 2. Lots of validation 3. Simple physics rules | |
| Earthquake | Almost impossible to predict time and location | 1. Very little sample data 2. Overfitting 3. Variables (like stress) in physics model can't be measured | ||
| GDP growth | Most economists failed | 1. Weak mechanism 2. Both input and output macro economic data not accurate 3. Policy impact | ||
| Epidemic | 1. Extrapolation with big prediction interval 2. When data is brought from one country to another, the underline condition has changed 3. Accurate simulation requires lots of input for human behavior | |||
| Basketball game | 1. Using Bayes rule to update propensity with new facts | |||
| Chess | 1. human vs machine. each has its own advantage human good at pattern identification and imagination machine good at in-depth thinking | |||
| Poker game | 1. Using Bayes rule to update propensity with new facts 2. Validation of forecast and differentiate skill with luck | |||
| Stock price | ||||
| Global warming | 1. Combine mechanisms with statistics | |||
| Terrorist attack | 1. Focus on frequency instead of when and where 2. For forecast of rare events, need imagination on scenarios |
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