We use a Monte Carlo approach to investigate the performance of several different methods designed to reduce the bias of the estimated coefficients for dynamic panel data models estimated with the longer, narrower panels typical of macro data. We find that the bias of the least squares dummy...

A problematic outcome of the cross sectional study of convergence is that developing economies are often estimated to be above their steady states, implying that they have too much capital per worker. This paper introduces a method to estimate steady states in a way that can explain this...

Since little is known about the degree of bias in estimated fixed effects in panel data models, we run Monte Carlo simulations on a range of different estimators. We find that Anderson-Hsiao IV, Kiviet's bias-corrected LSDV and GMM estimators all perform well in both short and long panels....

This paper reconsiders the nature of the trends (i.e. deterministic or stochastic) in the Nelson and Plosser (1982) series, both for the original and for an extended data set. For this purpose, I employ two tests that have been recently introduced by Enders and Lee (2006) and Becker et. al....

Unit roots in output, an exponential 2% rate of convergence and no change in the underlying dynamics of output seem to be three stylized facts that cannot go together. This paper extends the Solow-Swan growth model allowing for cross-sectional heterogeneity. In this framework, aggregate shocks...

The accuracy of particle filters for nonlinear state-space models crucially depends on the proposal distribution that mutates time t − 1 particle values into time t values. In the widely-used bootstrap particle filter this distribution is generated by the state- transition equation. While...

I estimate and evaluate a model with a representative agent who is concerned that the persistence properties of her baseline model of consumption and inflation are misspecified. Coping with model uncertainty, she discovers a pessimistically biased worst-case model that dictates her behavior. I...

This paper develops a DSGE model which explains variation in the nominal and real term structure along with inflation surveys and four macro variables in the UK economy. The model is estimated based on a third-order approximation to allow for time-varying term premia. We find a fall in nominal...

We develop a sequential Monte Carlo (SMC) algorithm for estimating Bayesian dynamic stochastic general equilibrium (DSGE) models, wherein a particle approximation to the posterior is built iteratively through tempering the likelihood. Using three examples -- an artificial state-space model, the...

We survey Bayesian methods for estimating dynamic stochastic general equilibrium (DSGE) models in this article. We focus on New Keynesian (NK) DSGE models because of the interest shown in this class of models by economists in academic and policy-making institutions. This interest stems from the...