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 …

Introduced more than a half-century ago, Granger causality has become a popular tool for
analyzing time series data in many application domains, from economics and finance to …

This is the first paper in a series of two that synthesizes, compares, and extends methods for
causal inference with longitudinal panel data in a structural equation modeling (SEM) …

This chapter reviews and synthesizes our current understanding of the shocks that drive
economic fluctuations. The chapter begins with an illustration of the problem of identifying …

In this study, we employ a TVP-FAVAR connectedness approach in order to investigate the
transmission mechanism in the cryptocurrency market. To this end, we concentrate on the …

We formulate a dynamic no-arbitrage asset pricing model for equities and corporate bonds,
featuring time variation in both risk aversion and economic uncertainty. The joint dynamics …

Predicting the dependencies between observations from multiple time series is critical for
applications such as anomaly detection, financial risk management, causal analysis, or …

This chapter provides an overview of and user's guide to dynamic factor models (DFMs),
their estimation, and their uses in empirical macroeconomics. It also surveys recent …

This paper employs an approximation that makes a nonlinear term structure model
extremely tractable for analysis of an economy operating near the zero lower bound for …

We present estimates of monetary non-neutrality based on evidence from high-frequency
responses of real interest rates, expected inflation, and expected output growth. Our …