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...

The early warning system literature on banking crises has often relied on linear classifiers such as the logit model, which are usually estimated with large datasets of multiple regions of countries. We construct an EWS based on an artificial neural network model with monthly data from the...

Regression discontinuity designs (RDD) are widely used in the social sciences to estimate causal effects from observational data. Scholars can choose from a range of methods that implement different RDD estimators, but there is a paucity of research on the performance of these different...

In this paper we ‘update’ the option implied probability of default (option iPoD) approach recently suggested in the literature. First, a numerically more stable objective function for the estimation of the risk neutral density is derived whose integrals can be solved analytically. Second,...

We propose a semi-parametric approach to heterogeneous dynamic panel data modelling. The method generalizes existing approaches to model cross-section homogeneity within such panels. It allows for partial influence of other cross-section units on estimated coefficients, differentiating between...

This article estimates the monetary policy rule followed by the Brazilian Central Bank for setting its main policy instrument, the SELIC rate, for the period after the Real Plan. In order to overcome the uncertainty over the dates at which changes in parameters occurred, this paper uses...

Time-variation in disagreement about inflation expectations is a stylized fact in surveys, but little is known on how disagreement interacts with the efficacy of monetary policy. This paper fills this gap in providing theoretical predictions of monetary policy shocks for different levels of...

A rapidly expanding universe of technology-focused startups is trying to change and improve the way real estate markets operate. The undisputed predictive power of machine learning (ML) models often plays a crucial role in the ‘disruption’ of traditional processes. However, an accountability...

Top distributions of income and wealth are still incompletely measured in many national statistics, particularly when using survey data. This paper develops the technique of incorporating the joint distributional relationship to enhance the estimation of these two top distributions by using the...