Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

Historically, time series forecasts of economic variables have used only a handful of
predictor variables, while forecasts based on a large number of predictors have been the …

Inflation forecasting is an important but difficult task. Here, we explore advances in machine
learning (ML) methods and the availability of new datasets to forecast US inflation. Despite …

Researchers have adopted online data such as search engine query volumes to forecast
tourism demand for a destination, including tourist numbers and hotel occupancy. However …

We use factor augmented vector autoregressive models with time-varying coefficients and
stochastic volatility to construct a financial conditions index that can accurately track …

The aim of this book is to investigate the spectral properties of random matrices (RM) when
their dimensions tend to infinity. All classical limiting theorems in statistics are under the …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

The term now-casting is a contraction for now and forecasting and has been used for a long
time in meteorology and recently also in economics. In this chapter we survey recent …

This paper shows that vector auto regression (VAR) with Bayesian shrinkage is an
appropriate tool for large dynamic models. We build on the results of De Mol and co‐workers …

This paper uses forecast combination methods to forecast output growth in a seven‐country
quarterly economic data set covering 1959–1999, with up to 73 predictors per country …