This chapter provides a thorough introduction to panel, global, and factor augmented vector autoregressive models. These models are typically used to capture interactions across units (i.e., countries) and variable types. Since including a large number of countries and/or variables increases the...

Bayesian vector autoregressions are widely used for macroeconomic forecasting and structural analysis. Until recently, however, most empirical work had considered only small systems with a few variables due to parameter proliferation concern and computational limitations. We first review a...

This Chapter reviews the main classes of models that incorporate volatility, with a focus on the most recent advancements in the financial econometrics literature and on the challenges posed by the increased availability of data. There are limits to the feasibility of all models when the...

In the past 10 years, neural networks have emerged as a powerful tool for predictive modeling with "big data." This chapter discusses the potential role of neural networks as applied to economic forecasting. It begins with a brief discussion of the history of neural networks, their use in...

This chapter covers penalized regression in the framework of linear time series models and reviews the most commonly used penalized estimators in applied work, namely Ridge Regression, the Least Absolute Shrinkage and Selection Operator (Lasso), the Elastic Net, the adaptive versions of the...

Factor models are widely used in macroeconomic forecasting. With large datasets, factor models are particularly useful due to their intrinsic dimension reduction. In this chapter, we consider the forecasting problem using factor models, with special consideration to large datasets. In factor...

This chapter summarises the recent approaches to optimal forecast combination from a frequentist perspective. The availability of big data leads to the development of many different models of the same macroeconomic variables. The challenge is to seek the best way to combine all relevant...

Bayesian model averaging (BMA) is a statistical method to rigorously take model uncertainty into account. This chapter gives a coherent overview on the statistical foundations and methods of BMA and its usefulness for forecasting, but also for the identification of robust determinants. The focus...