We propose a novel time-varying parameters mixed-frequency dynamic factor model which is integrated into a dynamic model averaging framework for macroeconomic nowcasting. Our suggested model can efficiently deal with the nature of the real-time data flow as well as parameter uncertainty and...

We propose a novel time-varying parameters mixed-frequency dynamic factor model which is integrated into a dynamic model averaging framework for macroeconomic nowcasting. Our suggested model can efficiently deal with the nature of the real-time data flow as well as parameter uncertainty and...

This paper investigates the asymmetries in arbitrage trading with onshore and offshore renminbi spot rates, focusing on the time-varying driving factors behind the deviations of the two rates from their long-run equilibrium. Fundamentally, offshore and onshore renminbi rates represent the same...

Financial asset prices contain a rich set of real-time information on the economy. To extract this information, it is crucial to understand the driving factors behind financial market developments. In this paper, we exploit daily cross-asset price movements in a sign-restricted BVAR model to...

We propose a multicountry quantile factor augmeneted vector autoregression (QFAVAR) to model heterogeneities both across countries and across characteristics of the distributions of macroeconomic time series. The presence of quantile factors allows for summarizing these two heterogeneities in a...

Financial asset prices contain a rich set of real-time information on the economy. To extract this information, it is crucial to understand the driving factors behind financial market developments. In this paper, we exploit daily cross-asset price movements in a sign-restricted BVAR model to...

This paper extends quantile factor analysis to a probabilistic variant that incorporates regularization and computationally efficient variational approximations. By means of synthetic and real data experiments it is established that the proposed estimator can achieve, in many cases, better...