The Hodrick-Prescott (HP) method is a popular smoothing method for economic time series to get a smooth or long-term component of stationary series like growth rates. We show that the HP smoother can be viewed as a Bayesian linear model with a strong prior using differencing matrices for the...

The Hodrick-Prescott (HP) method is a popular smoothing method for economic time series to get a longterm component of stationary series like growth rates. The new extended HP smoothing model is applied to data-sets with an underlying metric and requires a Bayesian linear regression model with a...

The forecast combination puzzle refers to the finding that a simple average forecast combination outperforms more sophisticated weighting schemes and/or the best individual model. The paper derives optimal (worst) forecast combinations based on stochastic dominance (SD) analysis with...

This paper proposes an infinite hidden Markov model (iHMM) to detect, date stamp, and estimate speculative bubbles. Three features make this new approach attractive to practitioners. first, the iHMM is capable of capturing the nonlinear dynamics of different types of bubble behaviors as it...

We consider a weighting scheme that yields the best-case scenario measurement of the Human Development Index (HDI) using an approach that relies on consistent tests for stochastic dominance efficiency (SDE). We compare a given hybrid composite index such as the official equally-weighted HDI to...

Hamiltonian Monte Carlo (HMC) is a recent statistical procedure to sample from complex distributions. Distant proposal draws are taken in a sequence of steps following the Hamiltonian dynamics of the underlying parameter space, often yielding superior mixing properties of the resulting Markov...

Missing data in dynamic panel models occur quite often since detailed recording of the dependent variable is often not possible at all observation points in time and space. In this paper we develop classical and Bayesian methods to complete missing data in panel models. The Chow-Lin (1971)...

This paper studies the application of the simulated method of moments (SMM) for the estimation of nonlinear dynamic stochastic general equilibrium (DSGE) models. Monte Carlo analysis is employed to examine the small-sample properties of SMM in specifications with different curvature. Results...

Vector autoregressions (VARs) are important tools in time series analysis. However, relatively little is known about the nite-sample behaviour of parameter estimators. We address this issue, by investigating ordinary least squares (OLS) estimators given a data generating process that is a purely...

We estimate the approximate nonlinear solution of a small DSGE model on euro area data, using the conditional particle filter to compute the model likelihood. Our results are consistent with previous findings, based on simulated data, suggesting that this approach delivers sharper inference...