The Conquest of U.S. Inflation: Learning and Robustness to Model Uncertainty

By Timothy Cogley, Thomas J. Sargent. Social Science Research Network, 2005

268 citations recorded by the source corpus.

Previous studies have interpreted the rise and fall of U.S. inflation after World War II in terms of the Fed's changing views about the natural rate hypothesis but have left an important question unanswered. Why was the Fed so slow to implement the low-inflation policy recommended by a natural rate model even after economists had developed statistical evidence strongly in its favor? Our answer features model uncertainty. Each period a central bank sets the systematic part of the inflation rate in light of updated probabilities that it assigns to three competing models of the Phillips curve. Cautious behavior induced by model uncertainty can explain why the central bank presided over the inflation of the 1970s even after the data had convinced it to place much the highest probability on the natural rate model. JEL Classification: E31, E58, E65

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/f9226296de27012577b53da4c21661ffe00b853d

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Citations in the corpus

Monetary/Fiscal Policy Mix and Agents’ Beliefs (2017, result): “This “stepping on a rake” effect represents a key ingredient in linking our results to the work of Primiceri (2006), Cogley and Sargent (2005), and Sargent et al. (2006).”
Central banks and financial crises (2008, background): “…domains, i cluding ‘trimmed mean’ measures and ‘approximate band pass filters’ will not be considered (see e.g. Bryan and Cecchetti (1994), Quah and Vahey (1995), Baxter and King (1999), Cogley (2002), Cogley and Sargent (2001, 2005)), Dolmas (2005), Rich and Steindel (2007), Kiley (2008)).”
The History of the Phillips Curve: Consensus and Bifurcation (2011, background): “Papers written by Sargent (1999), Cogley and Sargent (2005) and Sargent et al. (2006) begin with the standard presumption that choices by discretionary policy-makers will cause the PC to shift and policy options to change.”
Inference on stochastic time-varying coefficient models (2014, background): “The popular empirical choice of v t as i.i.d. sequence of random variables (with zero mean and variance σ 2 v ) corresponds to a driftless random walk a t (see, e.g. Cogley and Sargent (2005)).”
Model Averaging and Its Use in Economics (2017, methodology): “Cordero et al. (2016) use 60Here they follow the approach suggested by Amini and Parmeter (2012), who build on Magnus et al.”
Shocks and Government Beliefs: The Rise and Fall of American Inflation (2006, unknown): “Because our estimate of xt tracks the actual inflation path so well, our results are consistent with reduced-form empirical findings that changes in the policy rule or the inflation process are difficult to detect statistically (Cogley and Sargent, 2005b; Primiceri, 2005a; Sims and Zha, 2006).”
Learning and Macroeconomics (2009, background): “…Orphanides and Williams (2005a), which underscores both private-agent learning and policymaker misestimates of the natural rate of unemployment, and Cogley and Sargent (2005), which develops a historical account of inflation policy emphasizing Bayesian model averaging and learning by policymakers…”
Minimal State Variable Solutions to Markov-switching Rational Expectations Models (2011, background): “Furthermore, they find that once the model permits time variation in disturbance variances, there is no evidence in favor of policy changes at all (see also Cogley and Sargent (2005), Giorgio Primiceri (2005) and Uhlig (1997)).”