We consider the problem of optimally combining individual forecasts of gross domestic product (GDP) and inflation from the Survey of Professional Forecasters (SPF) dataset for the Euro Area. Contrary to the common practice of using equal combination weights, we compute optimal weights which...

Evaluation of forecast optimality in economics and finance has almost exclusively been conducted under the assumption of mean squared error loss. Under this loss function optimal forecasts should be unbiased and forecast errors serially uncorrelated at the single-period horizon with increasing...

Forecasts guide decisions in all areas of economics and finance and their value can only be understood in relation to, and in the context of, such decisions. We discuss the central role of the loss function in helping determine the forecaster's objectives and use this to present a unified...

This paper proposes new methodologies for evaluating out-of-sample forecasting performance that are robust to the choice of the estimation window size. The methodologies involve evaluating the predictive ability of forecasting models over a wide range of window sizes. We show that the tests...

A continuous monitoring of the evolution of the economy is fundamental for the decisions of public and private decision makers. This paper proposes a new monthly indicator of the euro area real Gross Domestic Product (GDP), with several original features. First, it considers both the output side...

We propose a new approach to predictive density modeling that allows for MIDAS effects in both the first and second moments of the outcome and develop Gibbs sampling methods for Bayesian estimation in the presence of stochastic volatility dynamics. When applied to quarterly U.S. GDP growth data,...

This paper introduces regime switching parameters in the Mixed-Frequency VAR model. We first discuss estimation and inference for Markov-switching Mixed-Frequency VAR (MSMF-VAR) models. Next, we assess the finite sample performance of the technique in Monte-Carlo experiments. Finally, the...

Mixed-data sampling (MIDAS) regressions allow to estimate dynamic equations that explain a low-frequency variable by high-frequency variables and their lags. When the difference in sampling frequencies between the regressand and the regressors is large, distributed lag functions are typically...

The debate on the forecasting ability of non-linear models has a long history, and the Great Recession episode provides us with an interesting opportunity for a reassessment of the forecasting performance of several classes of non-linear models. We conduct an extensive analysis over a large...

The term now-casting is a contraction for now and forecasting and has been used for a long-time in meteorology and recently also in economics In this paper we survey recent developments on economic now-casting with special focus on those models that formalize key features of how market...