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Abstract
This review tells the story of the past 20 years of time series econometrics through ten pictures. These pictures illustrate six broad areas of progress in time series econometrics: estimation of dynamic causal effects; estimation of dynamic structural models with optimizing agents (specifically, dynamic stochastic equilibrium models); methods for exploiting information in “big data” that are specialized to economic time series; improved methods for forecasting and for monitoring the economy; tools for modeling time variation in economic relationships; and improved methods for statistical inference. Taken together, the pictures show how 20 years of research have improved our ability to undertake our professional responsibilities. These pictures also remind us of the close connection between econometric theory and the empirical problems that motivate the theory, and of how the best econometric theory tends to arise from practical empirical problems.
Highlights
Twenty years ago, empirical macroeconomists shared some common understandings
These pictures illustrate six broad areas of progress in time series econometrics: estimation of dynamic causal effects; estimation of dynamic structural models with optimizing agents; methods for exploiting information in “big data” that are specialized to economic time series; improved methods for forecasting and for monitoring the economy; tools for modeling time variation in economic relationships; and improved methods for statistical inference
What is the effect of an autonomous, unexpected, policy-induced change in the monetary policy target rate—that is, a monetary policy shock—on output, prices, and other macro variables? The underlying problem is simultaneous causality: for example, the federal funds interest rate depends on changes in real GDP through a monetary policy rule, and GDP depends on the federal funds interest rate through induced changes in investment, consumption, and other variables
Summary
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. This review tells the story of the past 20 years of time series econometrics through ten pictures These pictures illustrate six broad areas of progress in time series econometrics: estimation of dynamic causal effects; estimation of dynamic structural models with optimizing agents (dynamic stochastic equilibrium models); methods for exploiting information in “big data” that are specialized to economic time series; improved methods for forecasting and for monitoring the economy; tools for modeling time variation in economic relationships; and improved methods for statistical inference. These pictures remind us that time series methods remain essential for shouldering real-world responsibilities. Our final figure, which is not from published research, illustrates an open empirical challenge for research ahead
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