Groen and Kapetanios suggest a way to perform parsimonious instrumental variables estimation in the presence of many, and potentially weak, instruments. In contrast to standard methods, the authors’ approach yields consistent estimates when the set of instrumental variables complies with a factor structure. In this sense, their method is equivalent to instrumental variables estimation that is based on principal components. However, even if the factor structure is weak or nonexistent, the authors’ method, unlike the principal components approach, still yields consistent estimates. Indeed, simulations indicate that their approach always dominates standard instrumental variables estimation, regardless of whether the factor relationship underlying the set of instruments is strong, weak, or absent.

The authors propose a Phillips-curve–type model that results from averaging across different regression specifications selected from a set of potential predictors. In each specification, they allow for stochastic breaks in regression parameters, where the breaks are described as occasional shocks of random magnitude. As such, their framework simultaneously addresses structural change and model uncertainty that unavoidably affect Phillips-curve–based predictions. Groen, Paap, and Ravazzolo use this framework to describe personal consumption expenditure (PCE) deflator and GDP deflator inflation rates for the United States in the post–World War II period. Over the full 1960-2008 sample, the framework indicates several structural breaks across different combinations of activity measures. These breaks often coincide with policy regime changes and oil price shocks, among other important events. In contrast to many previous studies, the authors find less evidence of autonomous variance breaks and inflation gap persistence. They also show that their model specification generally provides superior one-quarter-ahead and one-year-ahead forecasts for quarterly inflation.