My previous blog ended with a question about the usefulness of anticipating the long-term future if that future is highly uncertain. Ever since the 1982 article on “Trends and random walks in macroeconomic time series” by Nelson and Plosser, there has been a debate about the long-term statistical properties of GDP and other macroeconomic variables. Nelson and Plosser could not reject the hypothesis of a random walk (with drift), which means that random shocks have a permanent impact on the level of GDP and that the uncertainty interval around forecasts becomes wider and wider with every year you try to peek farther into the future. The message seems to be: If next year’s world is already very uncertain, don’t even bother forecasting the world in 2030.
Others found that “macroeconomic time series are best construed as stationary fluctuations around a deterministic trend function”, if you allow for a few structural breaks in the trend. The consequences for long-term forecasting are huge because, in this case, random shocks are transitory, there is mean reversion, and it is in fact easier to analyze long-term trends than short-term fluctuations.