Over the past decade there has been remarkable progress in developing empirical micro-founded macroeconomic models for monetary policy analysis that feature coherence both to economic theory and to the data. In this paper, we estimate using Bayesian methods a second-generation micro founded...

We present an algorithm and software routines for computing nth-order approximate solutions to dynamic, discrete-time rational expectations models around a nonstochastic steady state. We apply these routines to investigate the optimal monetary policy with commitment in an optimizing-agent model...

We formulate a generalized price-setting framework that incorporates staggered contracts of multiple durations and that enables us to directly identify the influences of nominal vs. real rigidities. Using German macroeconomic data over the period 1975Q1 through 1998Q4 to estimate this framework,...

Literature on monetary policy can be broadly classified into two categories. The first category involves the construction of dynamic stochastic general-equilibrium models for monetary policy. The solid micro foundations built into these models are important because they facilitate interpretation...

We construct an empirical measure of market frictions in the corporate market based on the difference between the corporate bond spread and the credit default swap spread for a large number of firms in a new, large dataset that we construct. Under fairly standard assumptions, the two spreads...

In this paper, we examine the cost of insurance against model uncertainty for the Euro area considering four alternative reference models, all of which are used for policy-analysis at the ECB. We find that maximal insurance across this model range in terms of a Minimax policy comes at moderate...

In this paper we consider expected value and mean variance optimization of a general forward--looking stochastic model. The problem is transformed into a general--nonlinear programming problem by adding extra constraints, which restrict the policy maker to commit to a certain policy. Based on...

This paper presents a numerical nonlinear dynamic programming algorithm for solving so-called optimal learning or adaptive control problems. These are decision problems with unknown parameters where the decisionmaker updates beliefs by Bayes rule. The updating equations are nonlinear. As a...