Monetary Policy, Parameter Uncertainty and Optimal Learning

By Volker Wieland. 2000

182 citations recorded by the source corpus.

Since central banks have limited information concerning the transmission channel of monetary policy, they are faced with the difficult task of simultaneously controlling the policy target and estimating the impact of policy actions. A tradeoff between estimation and control arises because policy actions influence estimation and provide information which may improve future performance. I analyze this tradeoff in a simple model with parameter uncertainty and conduct dynamic simulations of the policymaker's decision problem in the presence of the type of uncertainties that arose in the wake of German reunification. A policy that separates learning from control may induce a persistent upward bias in money growth and inflation, just as observed after unification. In contrast, the optimal learning strategy which exploits the tradeoff between control and estimation significantly improves stabilization performance and reduces the likelihood of inflationary bias.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/fb37cad22dd430190314adcd809534591045e874

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In Economics and Econometrics

Citations in the corpus

The Performance of Forecast-Based Monetary Policy Rules Under Model Uncertainty (2001, unknown): “…3 indicate the optimal values of θ and κ for the class of FB4 rules and the percent change in the loss function – denoted 20This tradeoff is characteristic of many macroeconomic models in the recent literature; cf. the papers in Taylor, ed (1999), and further discussion in Sack and Wieland (2000).”
Optimal Monetary Policy When Agents are Learning (2010, unknown): “Moreover, note that (41) and (42) lack any intrinsic source of inertia: if the cost-push shock is iid, so are inflation and output gap.”
ROBUST MONETARY POLICY UNDER MODEL UNCERTAINTY IN A SMALL MODEL OF THE U.S. ECONOMY (2002, background): “More complicated dynamic treatments incorporate learning, so that posteriors are continuously updated; cf. Wieland (1998), who builds on work by Wieland (2000), Easley and Kiefer (1988), and others.”
Monetary policy evaluation with noisy information (2003, background): “Integration of the process of learning from past experience by policymakers (as recently investigated by Sargent, 1998b, and Wieland, 1996) may be essential for a better understanding and evaluation of monetary policy.”