Optimal Monetary Policy Conclusions Ulf S¨oderstr¨om Bocconi University and Sveriges Riksbank [email protected] ulf.c.soderstrom.googlepages.com

Uppsala University, August 20, 2008

Optimal Monetary Policy

Conclusions

What is the effect of uncertainty on monetary policy? • Parameter uncertainty: – Uncertainty about effects of policy ⇒ optimal policy typically more cautious – Uncertainty about dynamics ⇒ optimal policy typically more aggressive – But exceptions in both cases • Model uncertainty: – Bayesian approach: No general conclusions – Avoid worst-case scenario ⇒ optimal policy often more aggressive • Data uncertainty: – Certainty equivalence to efficient estimate – Respond less to noisy indicator – But how do we know if estimate is efficient? • In general: Optimal policy is more or less aggressive depending on type and source of uncertainty

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Optimal Monetary Policy

Conclusions

Empirical importance • Parameter uncertainty – Does not seem to have large effects: Rudebusch (2001); Edge, Laubach, Williams (2007); Levin, Onatski, Williams, Williams (2005), Sala, S¨oderstr¨om, Trigari (2008) – Large effects if large model with many imprecisely estimated parameters: Sack (2000), S¨oderstr¨om (2000) • Rudebusch (2001): – Parameter uncertainty has small effects – Data uncertainty and model uncertainty have larger effects – Data and model uncertainty together can explain gradual Fed policy

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Optimal Monetary Policy

Conclusions

Learning • Central bank learning about private agents’ behavior • Private agents learning about the economy and monetary policy • Learning ⇒ more gradual behavior (cf. Kalman filter) More efficient if discretionary policy? (Dennis and Ravenna, 2008) • Experimentation: “Active learning” – Sacrifice stability today for more efficiency in future – Can imply more aggressive policy – Reasonable? Blinder (1998): “You don’t conduct experiments on a real economy solely to sharpen your econometric estimates” • Svensson and Williams (2008) – Optimal active and passive learning in small forward-looking model – Active learning computationally intensive – Gains from experimentation small: Active learning not very different from passive

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Optimal Monetary Policy

Conclusions

References Blinder, Alan S. (1998), Central Banking in Theory and Practice, The MIT Press. Dennis, Richard and Federico Ravenna (2008), “Learning and optimal monetary policy,” Journal of Economic Dynamics and Control , 32 (6), 1964–1994. Edge, Rochelle M., Thomas Laubach, and John C. Williams (2007), “Welfare-maximizing monetary policy under parameter uncertainty,” Working Paper No. 2007-11, Federal Reserve Bank of San Francisco. Levin, Andrew T., Alexei Onatski, John C. Williams, and Noah Williams (2005), “Monetary policy under uncertainty in microfounded macroeconometric models,” in Mark Gertler and Kenneth Rogoff (eds.), NBER Macroeconomics Annual , The MIT Press. Rudebusch, Glenn D. (2001), “Is the Fed too timid? Monetary policy in an uncertain world,” Review of Economics and Statistics, 83 (2), 203–217. Sack, Brian (2000), “Does the Fed act gradually? A VAR analysis,” Journal of Monetary Economics, 46 (1), 229–256. Sala, Luca, Ulf S¨oderstr¨om, and Antonella Trigari (2008), “Monetary policy under uncertainty in an estimated model with labor market frictions,” Journal of Monetary Economics, 55 (5), 983–1006. S¨oderstr¨om, Ulf (2000), “Should central banks be more aggressive?” Manuscript, Sveriges Riksbank. Svensson, Lars E. O. and Noah Williams (2008), “Optimal monetary policy under uncertainty in DSGE models: A Markov jump-linear-quadratic approach,” Working Paper No. 13892, National Bureau of Economic Research.

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