1. Graphics

2. Coin data How can we see what’s going on better? –Long run vs. short run

7. Graphing Graphing helps you see the relationship between variables Time series plots vs. scatter plots

14. Elements of Graphical Style Know your audience & know your goals Show the data and appeal to the viewer –Minimize non-data ink –Avoid chart junk Revise and edit, again and again

15. Non-data ink Note that after 1990, the pattern of velocity and opportunity cost changed significantly, with a couple of years of transition and a new pattern to the slope. The new intercept was much higher than the old one and thus the relationship changed so that it was much more difficult to use the model for forecasting.

16. Chartjunk

17. Don’t use 3 dimensions for a 2- dimensional object Don’t add decorations, cartoons, etc. that do not tell your story Hi, I’m irrelevant!

18. Make graphs tell your story The golden ratio of height to width is 0.618 Use scale to show variations in a variable

19. Velocity is very stable

20. Or is it unstable?

21. Use colors to split data

22. Or connect the dots to check timing

24. Beware of Outliers Measurement outliers –Data errors Innovation outliers –A shock or innovation

28. Adding recession bars Often help explain data well

31. Graphs as diagnostics for regressions Plot actual and fitted values; residuals over time Plot residuals squared or absolute values of residuals over time (solutions: interactive data analysis) Do a scatter plot of residual vs. explanatory variable

32. Example: consumption & income We can save residuals and do plots of residuals themselves, actual & predicted, residuals vs. explanatory variables Later, using saved residuals, we can plot squares and absolute values Note that non-random residuals suggests that a non-linear model may be better

34. Logs for trending variables When variables trend upwards, the graph of the variable shows too much recent information, not enough past information