Visualizing the 2018 World Economic Outlook Data

1. IMF World Economic Outlook
Data Visualization Hackathon
Presentation
(or, the only thing I remember from grad school is comparative
advantage)

2. Purpose of Today’s Analysis
• What can we learn quickly?
• How can I maximize the value of my presentation given the constraints of:
• Time (2hrs)
• Resources (Available Data)
• And the objective function:
• Visualize data
• Using my comparative advantage (data exploration & presentation)
• Minimize extraneous calculations
• This informs my technology (software choice)
• This informs my analysis (what data, series)

3. Resource Constraint
• More data than I have time to analyze
• Focus on three (3) data series of GDP growth (%
change)
• Advanced Economics
• Emerging Market and Developing Economies
• World Economy
• What can we learn quickly?

4. Technology
• Excel (sorry)
• Occam’s Razor meets Moore’s Law
• When presented with competing technological choices to solve a problem,
one should select the technology that requires the least amount of data
processing
• Most pre-processing was done(Thank you, IMF!), so quick
visualizations requires simple data wrangling and minimal calculations
• Take advantage of Excel’s ability to quickly transpose and manipulate
data tables and visualize time series
• What can we learn quickly?

5. Objective Function
•Visualize Data First
• Perspective
• Context
• New ways of thinking…
•Leverage “Ocular Economics”
• Good starting point for ‘deep dives’
•What can we learn quickly?

6. What Can We Learn Quickly?
•What can we visualize quickly?
•Time series data lends itself to line charts
•Small multiples
•What do we want to learn?
•How good are we at projecting GDP growth?
•Does it vary by vintage? By aggregated data
series?

7. Average Forecast Error by Vintage, by Series
-2.0%
-1.5%
-1.0%
-0.5%
0.0%
0.5%
Advanced Economies
Advanced Economies Spring Forecasts
Advanced Economies Fall Forecasts
-2.0%
-1.5%
-1.0%
-0.5%
0.0%
0.5%
Emerging Economies
Emerging Market and Developing Economies Spring Forecasts
Emerging Market and Developing Economies Fall Forecasts
-2.0%
-1.5%
-1.0%
-0.5%
0.0%
0.5%
World Economies
World Economy Spring Forecasts
World Economy Spring Forecasts

8. Average Forecast Error, by Vintage, by Series
Advanced
Economies
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Spring -1.8% -1.7% -0.3% -0.4% -0.7% -0.5% -0.3% -0.2% -0.2% 0.1% 0.3%
Fall -1.6% -1.3% -0.3% -0.5% -0.6% -0.3% -0.2% -0.2% 0.0% 0.3% 0.2%
Emerging
Economies
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Spring -0.9% -1.4% 0.0% -1.0% -1.6% -1.4% -1.2% -0.7% -0.2% 0.2% 0.3%
Fall -1.4% -1.3% -0.1% -1.1% -1.5% -1.1% -0.5% -0.4% 0.0% 0.2% 0.1%
World
Economy
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Spring -1.2% -1.3% 0.1% -0.5% -1.0% -0.8% -0.6% -0.3% -0.2% 0.1% 0.3%
Fall -1.5% -1.0% 0.0% -0.6% -0.9% -0.6% -0.2% -0.3% 0.0% 0.2% 0.1%

9. Forecast Error by Time Period, Spring Vintages
-6.0%
-5.0%
-4.0%
-3.0%
-2.0%
-1.0%
0.0%
1.0%
2.0%
3.0%
4.0%
t0 t1 t2 t3 t4 t5
Advanced Economies Forecast Error by
Time Period
Spring2007
Spring2008
Spring2009
Spring2010
Spring2011
Spring2012
Spring2013
Spring2014
Spring2015
Spring2016
Spring2017
-6.0%
-5.0%
-4.0%
-3.0%
-2.0%
-1.0%
0.0%
1.0%
2.0%
3.0%
4.0%
t0 t1 t2 t3 t4 t5
Emerging Economies Forecast Error by
Time Period
Spring2007
Spring2008
Spring2009
Spring2010
Spring2011
Spring2012
Spring2013
Spring2014
Spring2015
Spring2016
Spring2017
-6.0%
-5.0%
-4.0%
-3.0%
-2.0%
-1.0%
0.0%
1.0%
2.0%
3.0%
4.0%
t0 t1 t2 t3 t4 t5
World Economies Forecast Error by
Time Period, Spring Vintage
Spring2007
Spring2008
Spring2009
Spring2010
Spring2011
Spring2012
Spring2013
Spring2014
Spring2015
Spring2016
Spring2017

10. Forecast Error by Future Time Period, Fall Vintages
-6.0%
-5.0%
-4.0%
-3.0%
-2.0%
-1.0%
0.0%
1.0%
2.0%
3.0%
t0 t1 t2 t3 t4 t5
Advanced Economies Forecast Error by
Time Period
Fall2007
Fall2008
Fall2009
Fall2010
Fall2011
Fall2012
Fall2013
Fall2014
Fall2015
Fall2016
Fall2017
-6.0%
-5.0%
-4.0%
-3.0%
-2.0%
-1.0%
0.0%
1.0%
2.0%
3.0%
t0 t1 t2 t3 t4 t5
Emerging Economies Forecast Error by
Time Period
Fall2007
Fall2008
Fall2009
Fall2010
Fall2011
Fall2012
Fall2013
Fall2014
Fall2015
Fall2016
Fall2017
-6.0%
-5.0%
-4.0%
-3.0%
-2.0%
-1.0%
0.0%
1.0%
2.0%
3.0%
t0 t1 t2 t3 t4 t5
World Economy Forecast Error by
Time Period
Fall2007
Fall2008
Fall2009
Fall2010
Fall2011
Fall2012
Fall2013
Fall2014
Fall2015
Fall2016
Fall2017

11. Average Forecast Error by Future Time Period
Advanced
Economies
t0 t1 t2 t3 t4 t5
Spring Vintage -0.1% -0.8% -1.1% -0.8% -0.9% -0.8%
Fall Vintage 0.0% -0.6% -1.0% -0.7% -0.8% -0.7%
Emerging Economies t0 t1 t2 t3 t4 t5
Spring Vintage 0.2% -0.6% -1.2% -1.2% -1.6% -1.9%
Fall Vintage 0.0% -0.6% -1.0% -0.7% -0.8% -0.7%
World Economy t0 t1 t2 t3 t4 t5
Spring Vintage 0.2% -0.5% -1.0% -0.8% -1.1% -1.2%
Fall Vintage 0.3% -0.4% -0.9% -0.7% -1.0% -1.2%

12. Observations based on Visualizing the Data
• Fall Vintages have the highest accuracy, the most years out
• Vintages during/post the recession showed high variance in
predicting 1-3 years out
• Forecasts were over estimating GDP growth until 2015 (ish)
• Outliers from 2007+2008 forecasts drive average, so figure out a way
to treat them

13. Suggest Next Steps
• More Time
• More (Historical) Data
• Increase sample size of observations
• Econometric analysis
• Proper calibration of data
• Between vs. within vintage variances
• Between vs. within country series variances

14. Thank You!
Questions/Comments/Concerns
@incrmntlhppnnss
https://www.linkedin.com/in/thgrogan/