Modeling data revisions

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

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

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

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

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

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

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

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

How Big Are Benchmark Revisions? Examine Stark plots

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

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

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