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 of the most common techniques, including bridge equations, Mixed Data Sampling (MIDAS) models, mixed frequency...

We introduce a new approach for the estimation of high-dimensional factor models with regime-switching factor loadings by extending the linear three-pass regression filter to settings where parameters can vary according to Markov processes. The new method, denoted as Markov-switching three-pass...

Implications for signal extraction from specifying unobserved components (UC) models with correlated or orthogonal innovations have been well investigated. In contrast, the forecasting implications of specifying UC models with different state correlation structures are less well understood. This...

A longstanding finding in the forecasting literature is that averaging forecasts from different models often improves upon forecasts based on a single model, with equal weight averaging working particularly well. This paper analyzes the effects of trimming the set of models prior to averaging....

Predicting the economy's short-term dynamics-a vital input to economic agents' decisionmaking process-often uses lagged indicators in linear models. This is typically sufficient during normal times but could prove inadequate during crisis periods such as COVID-19. This paper demonstrates: (a)...

The COVID-19 pandemic and the resulting public health mitigation have caused large-scale economic disruptions globally. During this time, there is an increased need to predict the macroeconomy's short-term dynamics to ensure the effective implementation of fiscal and monetary policy. However,...

The use of "Big Data" to explain fluctuations in the broader economy or guide the business decisions of a firm is now so commonplace that in some instances it has even begun to rival more traditional government statistics and business analytics. Big data sources can very often provide advantages...

Recent rises in macroeconomic volatility have prompted the introduction of quantile vector autoregression (QVAR) models to forecast macroeconomic risk. This paper provides an extensive evaluation of the predictive performance of QVAR models in a pseudo-out-of-sample experiment spanning 112...

The prompt availability of information on the current state of the economy in real-time is required for prediction purposes and crucial for timely policy adjustment and economic decision-making. While important macroeconomic indicators are reported only quarterly and also published with...

Missing data is a problem appearing ubiquitously across many fields and needs to be dealt with systematically. For multivariate time series data imputation can be a challenging problem. We consider the particular case of credit default swap time series, where missing data can pose a considerable...