The central objective in structural VAR analysis is to quantify causal relationships in the data. Before discussing the identification of causal relationships in structural VAR models, it is useful to review the precursors to structural VAR analysis. Our discussion traces how the focus of the literature has evolved from documenting lead-lag patterns in the data, as discussed in Sections 7.2–7.4, to quantifying unanticipated shifts in the data reflecting exogenous events, as discussed in Section 7.5. There are several approaches to constructing such exogenous shocks. We review the narrative approach to measuring exogenous policy shocks, the derivation of exogenous shocks from data-based counterfactuals, the construction of news shocks from macroeconomic announcements, and the measurement of shocks to financial market expectations. The definition of exogenous shocks was generalized with the introduction of the structural VAR framework, as discussed in Section 7.6. The latter approach is based on decomposing fluctuations in the data that cannot be predicted based on past data into mutually uncorrelated exogenous shocks with economic interpretation that need not be directly observable. As we trace the evolution of this literature, we also formally introduce the concepts of predeterminedness, strict exogeneity, and Granger causality, highlighting the extent to which each approach relies on these concepts.

A Motivating Example

The need for structural models in studying causal relationships between economic time series is best illustrated by the debate about causality from monetary aggregates to national income in the 1960s and 1970s. It had long been observed that money growth and income growth in the United States were positively correlated. Based on a careful review of the historical evidence, Friedman and Schwartz (1963) in their Monetary History of the United States concluded that changes in money growth are causing changes in income growth (an obvious implication being that the Federal Reserve should pursue a constant money growth rule to stabilize the business cycle). This position evolved into a school of thought known as monetarism. Monetarism emphasizes the relation of the level of the money stock to the level of aggregate real economic activity (see Sims 1980b).

The monetarist position contrasted with the prevailing Keynesian wisdom that monetary policy was not nearly as important as fiscal policy in explaining economic fluctuations.