The aim of this paper is two-fold: (a) to establish a 'map' for describing the wide class of Limited Dependent Variables (LDV) univariate and multivariate models in the econometric literature and (b) to localize typical models in this tradition within the structure, extending typologies of...

The aim of this paper is two-fold: (a) to establish a ‘map’ for describing the wide class of Limited Dependent Variables (LDV) univariate and multivariate models in the econometric literature and (b) to localize typical models in this tradition within the structure, extending typologies of...

This paper extends the Bayesian Model Averaging framework to panel data models where the lagged dependent variable as well as endogenous variables appear as regressors. We propose a Limited Information Bayesian Model Averaging (LIBMA) methodology and then test it using simulated data. Simulation...

We develop monthly indicators for tracking growth in 32 advanced and emerging-market economies. We test the historical performance of our indicators and find that they do a good job at describing the business cycle. In a recursive out-of-sample forecasting exercise, we find that the indicators...

Macroeconomic policy decisions in real-time are based the assessment of current and future economic conditions. These assessments are made difficult by the presence of incomplete and noisy data. The problem is more acute for emerging market economies, where most economic data are released...

Financial conditions indexes are developed for the United States and euro area using a wide range of financial indicators and a dynamic factor model. The financial conditions indexes are shown to be useful for forecasting economic activity and have good revision properties.

The finite sample properties of the state space methods applied to long memory time series are analyzed through Monte Carlo simulations. The state space setup allows to introduce a novel modeling approach in the long memory framework, which directly tackles measurement errors and random level...