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16
Monetary Policy under Uncertainty
 IN MICROFOUNDED MACROECONOMETRIC MODELS,Â 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 217 (11 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.
2005a), “Monetary Policy with Judgment: Forecast Targeting
 International Journal of Central Banking
"... “Forecast targeting, ” forwardlooking monetary policy that uses centralbank judgment to construct optimal policy projections of the target variables and the instrument rate, may perform substantially better than monetary policy that disregards judgment and follows a given instrument rule. This i ..."
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Cited by 75 (28 self)
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“Forecast targeting, ” forwardlooking monetary policy that uses centralbank judgment to construct optimal policy projections of the target variables and the instrument rate, may perform substantially better than monetary policy that disregards judgment and follows a given instrument rule. This is demonstrated in a few examples for two empirical models of the U.S. economy, one forward looking and one backward looking. A complicated infinitehorizon centralbank projection model of the economy can be closely approximated by a simple finite system of linear equations, which is easily solved for the optimal policy projections. Optimal policy projections corresponding to the optimal policy under commitment in a timeless perspective can easily be constructed. The whole projection path of the instrument rate is more important than the current instrument setting. The resulting reducedform reaction function for the current instrument rate is a very complex function of all inputs in the monetarypolicy decision process, including the central bank’s judgment. It cannot be summarized as a simple reaction function such as a Taylor rule. Fortunately, it need not be made explicit.
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 66 (18 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
Bayesian and Adaptive Optimal Policy under Model Uncertainty.” CFS Working Paper No
, 2007
"... The Center for Financial Studies is a nonprofit research organization, supported by an association of more than 120 banks, insurance companies, industrial corporations and public institutions. Established in 1968 and closely affiliated with the University of Frankfurt, it provides a strong link betw ..."
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Cited by 28 (9 self)
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The Center for Financial Studies is a nonprofit research organization, supported by an association of more than 120 banks, insurance companies, industrial corporations and public institutions. Established in 1968 and closely affiliated with the University of Frankfurt, it provides a strong link between the financial community and academia. The CFS Working Paper Series presents the result of scientific research on selected topics in the field of money, banking and finance. This paper was presented at the
Anticipated Alternative InstrumentRate Paths in Policy Simulations
, 2009
"... This paper specifies how to do policy simulations with alternative instrumentrate paths in DSGE models such as Ramses, the Riksbank’s main model for policy analysis and forecasting. The new element is that these alternative instrumentrate paths are anticipated by the private sector. Such simulatio ..."
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Cited by 2 (0 self)
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This paper specifies how to do policy simulations with alternative instrumentrate paths in DSGE models such as Ramses, the Riksbank’s main model for policy analysis and forecasting. The new element is that these alternative instrumentrate paths are anticipated by the private sector. Such simulations correspond to situations where the Riksbank transparently announces that it plans to implement a particular instrumentrate path and where this announcement is believed by the private sector. Previous methods have instead implemented alternative instrumentrate paths by adding unanticipated shocks to an instrument rule, as in the method of modest interventions by Leeper and Zha (2003). This corresponds to a very different situation where the Riksbank would nontransparently and secretly plan to implement deviations from an announced instrument rule. In actual simulations, such deviations are normally both serially correlated and large, which seems inconsistent with the assumption that they would remain unanticipated by the private sector. Simulations with anticipated instrumentrate paths seem more relevant for the transparent flexible inflation targeting that the Riksbank conducts. We provide an algorithm for the computation of policy simulations with arbitrary restrictions on
Anticipated Alternative PolicyRate Paths in Policy Simulations
, 2010
"... This paper specifies a new convenient algorithm to construct policy projections conditional on alternative anticipated policyrate paths in linearized dynamic stochastic general equilibrium (DSGE) models, such as Ramses, the Riksbank’s main DSGE model. Such projections with anticipated policyrate p ..."
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Cited by 1 (0 self)
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This paper specifies a new convenient algorithm to construct policy projections conditional on alternative anticipated policyrate paths in linearized dynamic stochastic general equilibrium (DSGE) models, such as Ramses, the Riksbank’s main DSGE model. Such projections with anticipated policyrate paths correspond to situations where the central bank transparently announces that it, conditional on current information, plans to implement a particular policyrate path and where this announced plan for the policy rate is believed and then anticipated by the private sector. The main idea of the algorithm is to include among the predetermined variables (the “state”of the economy) the vector of nonzero means of future shocks to a given policy rule that is required to satisfy the given anticipated policyrate path.
BOP709.tex Bayesian and Adaptive Optimal Policy under Model Uncertainty ∗
"... We study the problem of a policymaker who seeks to set policy optimally in an economy where the true economic structure is unobserved, and he optimally learns from observations of the economy. This is a classic problem of learning and control, variants of which have been studied in the past, but sel ..."
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We study the problem of a policymaker who seeks to set policy optimally in an economy where the true economic structure is unobserved, and he optimally learns from observations of the economy. This is a classic problem of learning and control, variants of which have been studied in the past, but seldom with forwardlooking variables which are a key component of modern policyrelevant models. As in most Bayesian learning problems, the optimal policy typically includes an experimentation component reflecting the endogeneity of information. We develop algorithms to solve numerically for the Bayesian optimal policy (BOP). However, computing the BOP is only feasible in relatively small models, and thus we also consider a simpler specification we term adaptive optimal policy (AOP) which allows policymakers to update their beliefs but shortcuts the experimentation motive. In our setting, the AOP is significantly easier to compute, and in many cases provides a good approximation to the BOP. We provide some simple examples to illustrate the role of learning and experimentation in an MJLQ framework.
Carnegie2.tex Monetary Policy under Financial Uncertainty ∗
, 2011
"... www.ssc.wisc.edu/∼nwilliam ..."
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BOP606c.tex Preliminary and incomplete Bayesian and Adaptive Optimal Policy under Model Uncertainty
, 2006
"... We study the problem of a policymaker who seeks to set policy optimally in an economy where the true economic structure is unobserved, and policymakers optimally learn from their observations of the economy. This is a classic problem of learning and control, variants of which have been studied in th ..."
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We study the problem of a policymaker who seeks to set policy optimally in an economy where the true economic structure is unobserved, and policymakers optimally learn from their observations of the economy. This is a classic problem of learning and control, variants of which have been studied in the past, but little with forwardlooking variables which are a key component of modern policyrelevant models. As in most Bayesian learning problems, the optimal policy typically includes an experimentation component reflecting the endogeneity of information. We develop algorithms to solve numerically for the Bayesian optimal policy (BOP). However the BOP is only feasible in relatively small models, and thus we also consider a simpler specification we term adaptive optimal policy (AOP) which allows policymakers to update their beliefs but shortcuts the experimentation motive. In our setting, the AOP is significantly easier to compute, and in many cases provides a good approximation to the BOP. We provide a simple example to illustrate the role of learning and experimentation in an MJLQ framework. We also include an application of our methods to a relatively simple version of a benchmark NewKeynesian monetary model which is estimated from U.S data [to be done].
Exchange Rate Targeting in a Small Open Economy
, 2005
"... The paper develops a New Keynesian Small Open Economy Model characterized by external habit formation and Calvo price setting with dynamic ination updating. The model is used to analyze the e¤ect of nominal exchange rate targeting on optimal policy and impulse responses. It is found that even mode ..."
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The paper develops a New Keynesian Small Open Economy Model characterized by external habit formation and Calvo price setting with dynamic ination updating. The model is used to analyze the e¤ect of nominal exchange rate targeting on optimal policy and impulse responses. It is found that even moderate exchange rate concerns are capable of changing both sign and magnitude of the optimal instrument response to variables, and that whether the concern is with respect to the level or
rst di¤erence has much impact on monetary policy. Also, the cost of exchange rate stabilization in terms of output and ination is evident in the model, and impulse responses under moderate exchange rate targeting are not simple combinations of those under a oat and a regime that cares almost only for meeting the exchange rate target.