We introduce a mixed-frequency score-driven dynamic model for multiple time series where the score contributions from high-frequency variables are transformed by means of a mixed-data sampling weighting scheme. The resulting dynamic model delivers a flexible and easy-to-implement framework for...

We suggest to extend the stacking procedure for a combination of predictive densities, proposed by Yao, Vehtari, Simpson, and Gelman(2018), to a setting where dynamic learning occurs about features of predictive densities of possibly misspecified models. This improves the averaging process of...

Increasingly, professional forecasters and academic researchers present model-based and subjective or judgment-based forecasts in economics which are accompanied by some measure of uncertainty. In its most complete form this measure is a probability density function for future values of the...

A dynamic asset-allocation model is specified in probabilistic terms as a combination of return distributions resulting from multiple pairs of dynamic models and portfolio strategies based on momentum patterns in US industry returns. The nonlinear state space representation of the model allows...

Price fluctuations under adaptive learning in renewable resourcemarkets such as fisheries are examined. Optimal fisherymanagement with logistic fish pOpUlation growth implies a backward-bending, discounted supply curve for bioeconomicequilibrium sustained yield. Higher discount rates bend...

We propose a stylized exchange rate model based on diversity and weight ofopinion. Our model departs from standard assumptions in that we allow forheterogeneous agents. We show that such a model can explain both the observedvolatility and the persistence of real and nominal exchange rate...

We examine recursive out-of-sample forecasting of monthly postwarU.S. core inflation and log price levels. We use theautoregressive fractionally integrated moving average model withexplanatory variables (ARFIMAX). Our analysis suggests asignificant explanatory power of leading indicators...

The increasing availability of financial market data at intraday frequencies has not only led to the development of improved ex-post volatility measurements but has also inspired research into their potential value as an informa-tion source for longer horizon volatility forecasts. In this paper...

The multivariate analysis of a panel of economic and financial time series with mixed frequencies is a challenging problem. The standard solution is to analyze the mix of monthly and quarterly time series jointly by means of a multivariate dynamic model with a monthly time index: artificial...

Many empirical studies have shown that factor models produce relatively accurate forecasts compared to alternative short-term forecasting models. These empirical findings have been established for different macroeconomic data sets and different forecast horizons. However, various specifications...