1. Modeling data revisions

2. Monetary Policy: Analytical Revisions
What Happens When Economists or Policymakers Revise Conceptual Variables?
Output gap
Natural rate of unemployment
Equilibrium real interest rate
Concepts are never observed, but are centerpiece of macroeconomic theory

3. Monetary Policy: Analytical Revisions
Orphanides (2001): Fed overreacted to perceived output gap in 1970s, causing Great Inflation; but output gap was mismeasured

4. Output Gap Revisions
Most U.S. analysts look at CBO measure, but it is revised extensively over time
Problem is especially acute at the end of the sample

5. Potential Output
Potential output is a variable that we care about but cannot observe
As we get new data over time, we revise our view of potential output, using various statistical procedures
Example: CBO’s measure

10. Monetary Policy: Analytical Revisions
What Happens When Economists or Policymakers Revise Conceptual Variables?
Key issue: end-of-sample inference for forward-looking concepts (filters)
Key issue: optimal model of evolution of analytical concepts
Most work is statistical; perhaps a theoretical breakthrough is needed

11. Key Macroeconomic Variables
Real-Time Data Research Center at Federal Reserve Bank of Philadelphia
Conceptual Variables: Estimation implemented by Fed RAs
First one: Weekly macro factor from Aruoba-Diebold-Scotti

12. Key Macroeconomic Variables
We would like to add a useful measure of potential output
The CBO measure of potential output is convenient and has a track record (available on ALFRED)
The Real-Time Data Research Center would like to add a better measure

13. Big Problem: Benchmark Revisions
Cause sharp break in middle of “look ahead”
Can’t avoid them: benchmarks about every 5 years & look ahead = 5 years
Problem: benchmarks hit in same vintage but that is a different j for every t

14. How Big Are Benchmark Revisions?
Examine Stark plots

15. Stark Plots X(t,s) = level Plot log [X(t,b)/X(t,a)] — m, b>a
m = mean{log[X(τ,b)/X(τ,a)]}, b>a, for all τ in common
Trends
Spikes
Persistent deviations from linear trend

23. Stark Plots Result: Cross-vintage differences at many frequencies
Interpretation: Benchmark revisions matter considerably

24. How Big Are Benchmark Revisions?
Don’t revisions eventually settle down?
A few examples