Jani Luoto

Does Output Gap, Labour's Share or Unemployment Rate Drive Inflation?

We propose a new methodology for ranking in probability the commonly proposed drivers of inflation in the new Keynesian model. The approach is based on Bayesian model selection among restricted vector autoregressive (VAR) models, each of which embodies only one or none of the candidate variables as the driver. Simulation experiments suggest that our procedure is superior to the previously used conventional pairwise Granger causality tests in detecting the true driver. Empirical results lend little support to labour share, output gap or unemployment rate as the driver of US inflation.

A Noncausal Autoregressive Model with Time-Varying Parameters: An Application to U.S. Inflation

We propose a noncausal autoregressive model with time-varying parameters, and apply it to U.S. postwar inflation. The model .fits the data well, and the results suggest that inflation persistence follows from future expectations. Persistence has declined in the early 1980.s and slightly increased again in the late 1990.s. Estimates of the new Keynesian Phillips curve indicate that current inflation also depends on past inflation although future expectations dominate. The implied trend inflation estimate evolves smoothly and is well aligned with survey expectations. There is evidence in favor of the variation of trend inflation following from the underlying marginal cost that drives inflation.

Data-Driven Identification Constraints for DSGE Models

We propose imposing data†driven identification constraints to alleviate the multimodality problem arising in the estimation of poorly identified dynamic stochastic general equilibrium models under non†informative prior distributions. We also devise an iterative procedure based on the posterior density of the parameters for finding these constraints. An empirical application to the Smets and Wouters () model demonstrates the properties of the estimation method, and shows how the problem of multimodal posterior distributions caused by parameter redundancy is eliminated by identification constraints. Out†of†sample forecast comparisons as well as Bayes factors lend support to the constrained model.