Twenty Years of Time Series Econometrics in Ten Pictures

摘要

Twenty years ago, empirical macroeconomists shared some common understandings. One was that a dynamic causal effect—for example, the effect on output growth of the Federal Reserve increasing the federal funds rate—is properly conceived as the effect of a shock, that is, of an unanticipated autonomous change linked to a specific source. Following Sims (1980), the use of vector autore-gressions to estimate the dynamic causal effect of shocks on economic variables was widespread. There was also an understanding that vector autoregressions, because they impose as little structure on the data as possible, cannot answer questions about changes in policy regimes, such as the macroeconomic consequences of the Fed adopting a new policy rule. For such questions, more structured models grounded in economic theory are needed. At the same time, there was an increasing recognition that the available methods needed significant work. The schemes used to identify structural shocks in vector autoregressions were often seen as unconvincing by researchers outside the field, and the small structural models of the time were not econometrically estimated, miring that enterprise in an unhelpful debate over how to calibrate such models. In addition, there were chinks emerging in the theoretical econometric underpinnings of inference in time series data, as well as opportunities for using the much larger datasets becoming available, if only the tools to do so could be developed. The time was ripe for progress.