Given the rapid spread of the COVID-19 virus, the State has had to respond rapidly and quite severely to flatten the curve and slow the spread of the virus. This has had significant implications for many aspects of life with differential impacts across the population. The lack of timely...

Very low work intensity and at-risk-of-poverty are two of the three indicators used for monitoring progress towards the Europe 2020 poverty and social exclusion reduction target. Their timeliness is critical for tracing the effectiveness of policy interventions towards reaching this target....

Very low work intensity and at-risk-of-poverty are two of the three indicators used for monitoring progress towards the Europe 2020 poverty and social exclusion reduction target. Their timeliness is critical for tracing the effectiveness of policy interventions towards reaching this target....

This paper describes a dynamic factor model for the Maltese economy. The model mainly serves as a tool to timely provide the Central Bank of Malta with nowcasts as well as short-term forecasts of the growth rate of the real gross domestic product, which in turn are used as an input in the...

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...

We nowcast and forecast Austrian economic activity, namely real gross domestic product (GDP), consumption and investment, which are available at a quarterly frequency. While nowcasting uses data up to (and including) the quarter to be predicted, forecasting uses only data up to the previous...

In this study, we analyzed the forecasting and nowcasting performance of a generalized regression neural network (GRNN). We provide evidence from Monte Carlo simulations for the relative forecast performance of GRNN depending on the data-generating process. We show that GRNN outperforms an...

This paper presents a method to conduct early estimates of GDP growth in Germany. We employ MIDAS regressions to circumvent the mixed frequency problem and use pooling techniques to summarize efficiently the information content of the various indicators. More specifically, we investigate whether...

We present a medium-scale dynamic factor model to estimate and forecast the rate of growth of the Spanish economy in the very short term. The intermediate size of the model overcomes the serious specification problems associated with large scale-models and the implicit loss of information of...

In a pioneering attempt we present the R(ecession)-word index for Switzerland. We evaluate its predictive ability of GDP growth using real-time vintages of GDP data, closely simulating flow of information in the past. We find that inclusion of the R-word index led to statistically significant...