DSGE models are typically estimated using Bayesian methods, but a researcher may want to estimate a DSGE model with full information maximum likelihood (FIML) so as to avoid the use of prior distributions. A very robust algorithm is needed to find the global maximum within the relevant parameter...

The authors use first differenced logged quarterly series for the GDP of 29 countries and the euro area to assess the need to use nonlinear models to describe business cycle dynamic behaviour. Their approach is model (estimation)-free, based on testing only. The authors aim to maximize power to...

The procedure for estimating probabilities of future investment returns using time-shifted indexes is based on the simple principle that a multi-dimensional conditional probability distribution can be envisioned involving investment total returns (for a single investment or a fixed portfolio of...

We propose a new approach to mixed-frequency regressions in a high-dimensional environment that resorts to Group Lasso penalization and Bayesian techniques for estimation and inference. To improve the sparse recovery ability of the model, we also consider a Group Lasso with a spike-and-slab...

The procedure for estimating probabilities of future investment returns using time-shifted indexes is based on the simple principle that a multi-dimensional conditional probability distribution can be envisioned involving investment total returns (for a single investment or a fixed portfolio of...

We propose the adaptive elastic net for estimating Vector Autoregressions. Unlike competing methods, this estimator preserves the standard structural-VAR toolkit but at the same time leads to accurate forecasts. We show validity of the bootstrap in constructing unconditional-on-model-selection...

This paper explores the potential of Business Survey data for the estimation and disaggregation of macroeconomic variables at higher frequency. We propose a multivariate approach which is an extension of the Stock and Watson (1991) dynamic factor model, considering more than one common factor...

We reassess the predictability of U.S. recessions at horizons from three months to two years ahead for a large number of previously proposed leading-indicator variables. We employ an efficient probit estimator for partially missing data and assess relative model performance based on the receiver...

This paper illustrates the usefulness of sequential Monte Carlo (SMC) methods in approximating DSGE model posterior distributions. We show how the tempering schedule can be chosen adaptively, explore the benefits of an SMC variant we call generalized tempering for "online" estimation, and...

This paper illustrates the usefulness of sequential Monte Carlo (SMC) methods in approximating DSGE model posterior distributions. We show how the tempering schedule can be chosen adaptively, explore the benefits of an SMC variant we call generalized tempering for “online” estimation, and...