Results 1  10
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19
Monetary Policy under Uncertainty
 in MicroFounded Macroeconometric Models,” NBER Macroeconomics Annual
, 2005
"... We use a microfounded macroeconometric modeling framework to investigate the design of monetary policy when the central bank faces uncertainty about the true structure of the economy. We apply Bayesian methods to estimate the parameters of the baseline specification using postwar U.S. data and then ..."
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Cited by 133 (9 self)
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We use a microfounded macroeconometric modeling framework to investigate the design of monetary policy when the central bank faces uncertainty about the true structure of the economy. We apply Bayesian methods to estimate the parameters of the baseline specification using postwar U.S. data and then determine the policy under commitment that maximizes household welfare. We find that the performance of the optimal policy is closely matched by a simple operational rule that focuses solely on stabilizing nominal wage inflation. Furthermore, this simple wage stabilization rule is remarkably robust to uncertainty about the model parameters and to various assumptions regarding the nature and incidence of the innovations. However, the characteristics of optimal policy are very sensitive to the specification of the wage contracting mechanism, thereby highlighting the importance of additional research regarding the structure of labor markets and wage determination.
Inflation Targeting
, 2010
"... Inflation targeting is a monetarypolicy strategy that is characterized by an announced numerical inflation target, an implementation of monetary policy that gives a major role to an inflation forecast and has been called forecast targeting, and a high degree of transparency and accountability. It w ..."
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Cited by 98 (12 self)
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Inflation targeting is a monetarypolicy strategy that is characterized by an announced numerical inflation target, an implementation of monetary policy that gives a major role to an inflation forecast and has been called forecast targeting, and a high degree of transparency and accountability. It was introduced in New Zealand in 1990, has been very successful in terms of stabilizing both inflation and the real economy, and has, as of 2010, been adopted by about 25 industrialized and emergingmarket economies. The chapter discusses the history, macroeconomic effects, theory, practice, and future of inflation targeting.
Back to Square One: Identification Issues in DSGE Models", mimeo
"... publications will feature a motif taken from the €5 banknote. This paper can be downloaded without charge from ..."
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Cited by 47 (1 self)
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publications will feature a motif taken from the €5 banknote. This paper can be downloaded without charge from
2007a, Monetary policy with model uncertainty: distribution forecast targeting, unpublished manuscript
"... We examine optimal and other monetary policies in a linearquadratic setup with a relatively general form of model uncertainty, socalled Markov jumplinearquadratic systems extended to include forwardlooking variables and unobservable “modes. ” The form of model uncertainty our framework encompas ..."
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Cited by 40 (12 self)
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We examine optimal and other monetary policies in a linearquadratic setup with a relatively general form of model uncertainty, socalled Markov jumplinearquadratic systems extended to include forwardlooking variables and unobservable “modes. ” The form of model uncertainty our framework encompasses includes: simple i.i.d. model deviations; serially correlated model deviations; estimable regimeswitching models; more complex structural uncertainty about very different models, for instance, backward and forwardlooking models; timevarying centralbank judgment about the state of model uncertainty; and so forth. We provide an algorithm for finding the optimal policy as well as solutions for arbitrary policy functions. This allows us to compute and plot consistent distribution forecasts—fan charts—of target variables and instruments. Our methods hence extend certainty equivalence and “mean forecast targeting ” to more general certainty nonequivalence and “distribution forecast targeting.” JEL Classification: E42, E52, E58
Anticipated Utility and Rational Expectations as Approximations of Bayesian Decion Making
, 2005
"... We study a Markov decision problem with unknown transition probabilities. We compute the exact Bayesian decision rule and compare it with two approximations. The first is an infinitehistory, rationalexpectations approximation that assumes that the decision maker knows the transition probabilities. ..."
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Cited by 30 (3 self)
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We study a Markov decision problem with unknown transition probabilities. We compute the exact Bayesian decision rule and compare it with two approximations. The first is an infinitehistory, rationalexpectations approximation that assumes that the decision maker knows the transition probabilities. The second is a version of Kreps ’ (1998) anticipatedutility model in which decision makers update using Bayes ’ law but optimize in a way that is myopic with respect to their updating of probabilities. For several consumptionsmoothing examples, the anticipatedutility approximation outperforms the rational expectations approximation. The rational expectations approximation misrepresents the market price of risk in a Bayesian economy. Key words: Rational expectations, Bayes ’ Law, anticipated utility, market price of risk. ∗ For comments and suggestions, we thank Lars Hansen, Narayana Kocherlakota, Frank Schorfheide, three referees, and seminar participants at Stanford and the CFS Summer School
Recursive Robust Estimation and Control Without Commitment
, 2006
"... In a Markov decision problem with hidden state variables, a posterior distribution serves as a state variable and Bayes ’ law under an approximating model gives its law of motion. A decision maker expresses fear that his model is misspecified by surrounding it with a set of alternatives that are nea ..."
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Cited by 29 (7 self)
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In a Markov decision problem with hidden state variables, a posterior distribution serves as a state variable and Bayes ’ law under an approximating model gives its law of motion. A decision maker expresses fear that his model is misspecified by surrounding it with a set of alternatives that are nearby when measured by their expected log likelihood ratios (entropies). Martingales represent alternative models. A decision maker constructs a sequence of robust decision rules by pretending that a sequence of minimizing players choose increments to a martingale and distortions to the prior over the hidden state. A risk sensitivity operator induces robustness to perturbations of the approximating model conditioned on the hidden state. Another risk sensitivity operator induces robustness to the prior distribution over the hidden state. We use these operators to extend the approach of Hansen and Sargent (1995) to problems that contain hidden states. 1
Evolution and intelligent design
 American Economic Review
, 2008
"... This paper discusses two sources of ideas that influence monetary policy makers today. The first is a set of analytical results that impose the rational expectations equilibrium concept and do ‘intelligent design ’ by solving Ramsey and mechanism design problems. The second is the adaptive learning ..."
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Cited by 21 (1 self)
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This paper discusses two sources of ideas that influence monetary policy makers today. The first is a set of analytical results that impose the rational expectations equilibrium concept and do ‘intelligent design ’ by solving Ramsey and mechanism design problems. The second is the adaptive learning process that first taught us how to anchor the price level with a gold standard, then how to replace the gold standard with a fiat currency wanting nominal anchors. Models of outofequilibrium learning say that such an adaptive evolutionary process will converge to a selfconfirming equilibrium (SCE). In an SCE, a government’s probability model is correct about events that occur under the prevailing government policy, but possibly wrong about the consequences of other policies. That causes mistakes absent from a rational expectations equilibrium and expands the role of learning.
Robust Estimation and Control under Commitment
 Journal of Economic Theory
, 2005
"... When Shannon had invented his quantity and consulted von Neumann on what to call it, von Neumann replied: ‘Call it entropy. It is already in use under that name and besides, it will give you a great edge in debates because nobody knows what entropy is anyway. ’ 1 In a Markov decision problem with hi ..."
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Cited by 16 (7 self)
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When Shannon had invented his quantity and consulted von Neumann on what to call it, von Neumann replied: ‘Call it entropy. It is already in use under that name and besides, it will give you a great edge in debates because nobody knows what entropy is anyway. ’ 1 In a Markov decision problem with hidden state variables, a decision maker expresses fear that his model is misspecified by surrounding it with a set of alternatives that are nearby as measured by their expected log likelihood ratios (entropies). Sets of martingales represent alternative models. Within a twoplayer zerosum game under commitment, a minimizing player chooses a martingale at time 0. Probability distributions that solve distorted filtering problems serve as state variables, much like the posterior in problems without concerns about misspecification. We state conditions under which an equilibrium of the zerosum game with commitment has a recursive representation that can be cast in terms of two risksensitivity operators. We apply our results to a linear quadratic example that makes contact with findings of Basar
2008): “Robustness and U.S. Monetary Policy Experimentation
 Journal of Money, Credit, and Banking
"... We study how a concern for robustness modifies a policy maker’s incentive to experiment. A policy maker has a prior over two submodels of inflationunemployment dynamics. One submodel implies an exploitable tradeoff, the other does not. Bayes ’ law gives the policy maker an incentive to experiment. ..."
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Cited by 6 (5 self)
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We study how a concern for robustness modifies a policy maker’s incentive to experiment. A policy maker has a prior over two submodels of inflationunemployment dynamics. One submodel implies an exploitable tradeoff, the other does not. Bayes ’ law gives the policy maker an incentive to experiment. The policy maker fears that both submodels and his prior probability distribution over them are misspecified. We compute decision rules that are robust to misspecifications of each submodel and of the prior distribution over submodels. We compare robust rules to ones that Cogley, Colacito, and Sargent (2007) computed assuming that the models and the prior distribution are correctly specified. We explain how the policy maker’s desires to protect against misspecifications of the submodels, on the one hand, and misspecifications of the prior over them, on the other, have different effects on the decision rule.
A Bayesian Approach to Optimal Monetary Policy with Parameter and Model Uncertainty ∗
"... This paper undertakes a Bayesian analysis of optimal monetary policy for the U.K. We estimate a suite of monetarypolicy models that include both forwardand backwardlooking representations as well as large and smallscale models. We find an optimal simple Taylortype rule that accounts for both mo ..."
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Cited by 1 (0 self)
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This paper undertakes a Bayesian analysis of optimal monetary policy for the U.K. We estimate a suite of monetarypolicy models that include both forwardand backwardlooking representations as well as large and smallscale models. We find an optimal simple Taylortype rule that accounts for both model and parameter uncertainty. For the most part, backwardlooking models are highly fault tolerant with respect to policies optimized for forwardlooking representations, while forwardlooking models have low fault tolerance with respect to policies optimized for backwardlooking representations. In addition, backwardlooking models often have lower posterior probabilities than forwardlooking models. Bayesian policies therefore have characteristics suitable for inflation and output stabilization in forwardlooking models. 1