Some economists advocate nominal GDP targeting as an alternative to the Taylor Rule. These arguments are largely based on the idea that nominal GDP targeting would require less knowledge on the part of policymakers than a traditional Taylor Rule. In particular, a nominal GDP targeting rule would not require real‐time knowledge of the output gap. We examine the importance of this claim by amending a standard New Keynesian model to assume that the central bank has imperfect information about the output gap and therefore must forecast the output gap based on previous information. Forecast errors by the central bank can then potentially induce unanticipated changes in the short‐term nominal interest rate, distinct from a standard monetary policy shock. We show that forecast errors of the output gap by the Federal Reserve can account for up to 13% of the fluctuations in the output gap. In addition, our simulations imply that a nominal GDP targeting rule would produce lower volatility in both inflation and the output gap in comparison with the Taylor Rule under imperfect information.
Firm characteristics, economic conditions and policy regimes are the key determinants that most researchers have used to explain corporate bond yield spreads. In this article, we examine whether monetary policy shocks are also important determinants given their ability to affect default risk, risk aversion and liquidity premiums. Using a Vector Autoregression (VAR) with long-run monetary neutrality, we find that monetary policy shocks do, in fact, account for a large portion of the variation in corporate bond yield spreads.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.