Monitoring and forecasting price developments in the euro area is essential in the light of the second pillar of the ECBu0092s monetary policy strategy. This study analyses whether the forecasting accuracy of forecasting aggregate euro area inflation can be improved by aggregating forecasts of...

We extend a standard multivariate filter used to estimate the Output Gap (OG) in Chile to account for large economic shocks, such as those observed during the COVID-19 crisis. We propose two methodological extensions. First, we introduce exogenous supply shocks in the dynamics of potential...

We study the out-of-sample forecasting performance of 32 exchange rates vis-a-vis the New Taiwan Dollar (NTD) in a 32-variable vector autoregression (VAR) model. The Bayesian approach is applied to the large-scale VAR model (LBVAR), and its (timevarying) forecasting performance is compared to...

We analyse the accuracy of an econometric model for nowcasting GDP growth in a true real-time setting. The analysis is based on a unique sample of nowcasts that were produced in real time and stored. Our results support the use of econometric models for nowcasting because the accuracy of these...

DSGE models are often specified so that the long-run variation of variables is driven by one or two common trends, which rarely holds in the data. We find that when this discrepancy exists, high-frequency components (measurement errors) capture variable-specific time variation in trends. When...

We study the use of a misspecified overdifferenced model to forecast the level of a stationary scalar time series. Let x(t) be the series, and let bias be the sample average of a series of forecast errors. Then, the bias of forecasts of x(t) generated by a misspecified overdifferenced ARMA model...

We propose an easy-to-implement framework for combining quantile forecasts, applied to forecasting GDP growth. Using quantile regressions, our combination scheme assigns weights to individual forecasts from different indicators based on quantile scores. Previous studies suggest distributional...

The paper compares two forecasts of Slovak GDP, the first with high-frequency data and the second without them. We utilize the last observation from the economic activity index acting as a short-term GDP forecast. We use data from 2000 to 2024 in weekly frequencies and have 27 variables related...

This paper addresses the poor performance of the Expectation-Maximization (EM) algorithm in the estimation of low-noise dynamic factor models, commonly used in macroeconomic forecasting and nowcasting. We show analytically and in Monte Carlo simulations how the EM algorithm stagnates in a...

Quantile forecasting has become an important research topic in econometrics as policy makers and investors are increasingly interested to focus more on downside (upside) risks rather than learning about the most likely outcome. Simultaneously, practitioners have largely used textual data to con-...