A simple recursive forecasting model

TL;DR: In this paper, the authors present an estimated model that departs from rational expectations and nests learning by economic agents, habits, and indexation, showing that when learning replaces rational expectations, the estimated degrees of habits and inflation indexation drop closer to zero, suggesting that persistence arises in the model economy mainly from expectations and learning.

TL;DR: The authors argue for a careful re-consideration of the expectations formation process and a more systematic inclusion of real-time expectations through survey data in macroeconomic analyses, using the New Keynesian Phillips curve as an extensive case study.

TL;DR: In this article, an alternative theory based on learning dynamics is explored, where households and firms have an incomplete model of the macroeconomy, knowing only their own objectives, constraints, and beliefs.

TL;DR: The authors decompose Treasury yields into long-horizon inflation expectations and maturity related cycles, and construct a measure of expected bond returns from cycles, which captures the predictable element of bond returns usually measured with a linear combination of forward rates.

TL;DR: Methods of recursive identification deal with the problem of building mathematical models of signals and systems on-line, at the same time as data is being collected.

TL;DR: In this paper, the authors propose a statistical learning approach to predict the evolution of expectations and selection between alternative equilibria, with implications for business cycles, asset price volatility, and policy.

TL;DR: For a VAR with drifting coefficients and stochastic volatilities, the authors present posterior densities for several objects that are pertinent for designing and evaluating monetary policy, including measures of inflation persistence, the natural rate of unemployment, a core rate of inflation, and ‘activism coefficients' for monetary policy rules.

TL;DR: This paper developed a forecasting procedure based on a Bayesian method for estimating vector autoregressions, which is applied to 10 macroeconomic variables and is shown to improve out-of-sample forecasts relative to univariate equations.