Measuring Uncertainty about Long-Run Predictions

S-Tier
Journal: Review of Economic Studies
Year: 2016
Volume: 83
Issue: 4
Pages: 1711-1740

Authors (2)

Ulrich K. Müller (not in RePEc) Mark W. Watson (Princeton University)

Score contribution per author:

4.022 = (α=2.01 / 2 authors) × 4.0x S-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

Abstract

Long-run forecasts of economic variables play an important role in policy, planning, and portfolio decisions. We consider forecasts of the long-horizon average of a scalar variable, typically the growth rate of an economic variable. The main contribution is the construction of prediction sets with asymptotic coverage over a wide range of data generating processes, allowing for stochastically trending mean growth, slow mean reversion, and other types of long-run dependencies. We illustrate the method by computing prediction sets for 10- to 75-year average growth rates of U.S. real per capita GDP and consumption, productivity, price level, stock prices, and population.

Technical Details

RePEc Handle
repec:oup:restud:v:83:y:2016:i:4:p:1711-1740.
Journal Field
General
Author Count
2
Added to Database
2026-01-29