Washington, DC – Federal Reserve Board member Matteo commended the audience at the 13th International Research Forum on Monetary Policy, emphasizing the importance of understanding uncertainty in monetary policy decisions. Reflecting on the challenges faced during the pandemic, Matteo highlighted the significance of grasping the sources of uncertainty for effective policymaking.
Discussing the evolution of economic thinking on monetary policy amid uncertainty, Matteo drew attention to the 1960s to 1980s era marked by the Keynesian macroeconomics tradeoff between inflation and unemployment. Notable figures like Milton Friedman emphasized considering uncertainty in decision-making processes, advocating for simple rules to guide monetary policy as opposed to fine-tuning. The importance of uncertainty in shaping policy responses was evident in historical contexts.
In the 1990s and 2000s, the emergence of a “new economy” introduced complexities in policy-making. Notably, the debate around the effectiveness of simple policy rules gained momentum, with John Taylor’s rule serving as a reference point. The literature on learning in macroeconomics expanded understanding of uncertainty, emphasizing the need for context-specific responses to uncertainty.
The Global Financial Crisis of 2008 highlighted the delicate balance between caution and assertive action in monetary policy amid uncertainty. The crisis underscored the importance of adaptability and proactive measures to stabilize the economy. The lessons learned from past crises continue to inform current policy decisions, particularly in the face of persistent inflation and economic growth challenges.
Looking at the current economic landscape, Matteo acknowledged the progress in lowering inflation but emphasized the ongoing work needed to sustainably restore the target inflation rate. With real GDP growth showing resilience and job gains surpassing expectations, the outlook remains uncertain, calling for a balanced approach to policy.
In conclusion, Matteo stressed the critical role of understanding uncertainty in influencing economic outcomes. Encouraging continued research and innovation in this field, Matteo lauded the audience for their contributions to enriching policymakers’ knowledge and decision-making processes.
1. The views expressed here are my own and are not necessarily those of my colleagues on the Federal Reserve Board or the Federal Open Market Committee.
2. For example, in 1998, Alan Blinder wrote that the Brainard result was “never far from my mind when I occupied the Vice Chairman’s office at the Federal Reserve. In my view . . . a little stodginess at the central bank is entirely appropriate.”
3. An incomplete list of the early contributions includes Prescott (1972), Chow (1976), Craine and Havenner (1977), and Kendrick (1982).
4. The estimate of 9.3 million jobs uses total nonfarm seasonally adjusted numbers from the St. Louis Fed FRED database (PAYEMS series) and takes the difference between the December 1998 and January 1996 numbers.
5. Optimal-control policies are optimal conditional on the structure of the model to which they are applied. Provided the model is a reliable approximation of the true economy, and policymakers’ preferences are correctly specified, the policy prescriptions derived from optimal-control exercises are, by definition, the best that can be achieved. However, the underlying assumptions are stringent in many applications. There is also a literature on risk-adjusted optimal control dating back at least to Whittle (1981).
6. Nikolosko-Rzhevskyy, Papell, and Prodan (2014), among others, explore this question.
7. By “learnable” I mean in the sense that least-squares learning would converge, in the limit, on rational expectations equilibrium.
8. See, for example, Stock and Watson (2021) for time-series evidence and Smith, Timmermann, and Wright (2023) for panel data results.
9. See, for example, Bullard (2018) for a demonstration and McLeay and Tenreyo (2019) for a detailed argument along these lines.
10. The literature on robust control in economics (for example, Hansen and Sargent, 2008) began with the normative case of how policymakers might address their doubts. The literature on ambiguity aversion (for example, Epstein and Schneider, 2003) had agents within models confront their doubts in making decisions. Works at the intersection of these two strands of the literature expand the concept of uncertainty (in the sense of Knight, 1921) in a micro-founded manner and relax the rational expectations hypothesis in a disciplined way. See, for example, Hansen and Sargent (2021).
11. See Barlevy (2011) for an accessible survey and Tetlow and von zur Muehlen (2001) for a more technical treatment.
12. Söderstöm (2002) establishes the result for the Bayesian case. Tetlow (2019) is a simple demonstration of that case alongside an ambiguity aversion case. With uncertain inflation persistence, the Bayesian and ambiguity aversion approaches lead to a policy that is stronger than the certainty equivalent case because a symmetric distribution for inflation persistence results in asymmetric (downward-skewed) distributions for economic outcomes when evaluated for the certainty equivalent policy.
13. See paragraphs 2 and 4 of the September 2007 FOMC statement.
14. See paragraph 4 of the June 2008 FOMC statement.
15. The literature on the implications of the effective lower bound on nominal interest rates for the conduct on monetary policy is large.
16. See Powell (2018).