Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
Inflation targeting as a monetary-policy regime is widely associated with an explicit numerical target for the rate of inflation. This paper investigates whether the forecasting performance of Bayesian autoregressive models can be improved by incorporating information about the target. We compare a mean-adjusted specification, which allows an informative prior on the distribution for the steady state of the process, to traditional methodology. We find that the out-of-sample forecasts of the mean-adjusted autoregressive model outperform those of the traditional specification, often by non-trivial amounts, for five early adopters of inflation targeting. It is also noted that as the sample lengthens, the posterior distribution of steady-state inflation narrows more for countries with explicit point targets.