This paper investigates the effect of the COVID-19 and oil prices on the US partisan conflict.
Using daily data on world COVID-19 and oil prices, monthly data on the US Partisan Conflict …

Based on high-frequency data for more than fifty commodities, currencies, equity indices,
and fixed-income instruments spanning more than two decades, we document strong …

This article introduces structured machine learning regressions for high-dimensional time
series data potentially sampled at different frequencies. The sparse-group LASSO estimator …

We consider simple methods to improve the growth nowcasts and forecasts obtained by
mixed-frequency MIDAS and UMIDAS models with a variety of indicators during the Covid …

This article develops a vector autoregression (VAR) for time series which are observed at
mixed frequencies—quarterly and monthly. The model is cast in state-space form and …

Many time series are sampled at different frequencies. When we study co-movements
between such series we usually analyze the joint process sampled at a common low …

The goal of this article is to evaluate the informational content of sentiment extracted from
news articles about the state of the economy. We propose a fine-grained aspect-based …

The mixed data sampling (MIDAS) model has attracted increasing attention due to its
outstanding performance in dealing with mixed frequency data. However, most MIDAS …

This paper is concerned with an introduction to big data which can be potentially used in
nowcasting the UK GDP and other key macroeconomic variables. We discuss various big …

The development of models for variables sampled at different frequencies has attracted
substantial interest in the recent econometric literature. In this paper we provide an overview …