Target Criterion

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Michael Woodford - One of the best experts on this subject based on the ideXlab platform.

  • Robustly Optimal Monetary Policy in a New Keynesian Model with Housing
    2020
    Co-Authors: Klaus Adam, Michael Woodford
    Abstract:

    We analytically characterize optimal monetary policy for an augmented New Keynesian model with a housing sector. With rational private sector expectations about housing prices and inflation, optimal monetary policy can be characterized by a standard “Target Criterion” in terms of inflation and the output gap, that makes no reference to housing prices. If instead the policymaker is concerned with potential departures of private sector expectations from rational ones, and seeks a policy that is robust against such possible departures, then the optimal Target Criterion will also depend on housing prices. For empirically realistic cases, robustness requires the central bank to “lean against” housing prices, i.e., to adopt a stance that is projected to undershoot (overshoot) its normal Targets for inflation and the output gap following unexpected housing price increases (decreases). Notably, robustly optimal policy does not require that the central bank distinguish between “fundamental” and “non-fundamental” movements in housing prices.

  • Leaning Against Housing Prices as Robustly Optimal Monetary Policy
    2018
    Co-Authors: Klaus Adam, Michael Woodford
    Abstract:

    We analytically characterize optimal monetary policy for an augmented New Keynesian model with a housing sector. In a setting where the private sector has rational expectations about future housing prices and inflation, optimal monetary policy can be characterized without making reference to housing price developments: commitment to a ‘Target Criterion’ that refers to inflation and the output gap only is optimal, as in the standard model without a housing sector. When the policymaker is concerned with potential departures of private sector expectations from rational ones and seeks to choose a policy that is robust against such possible departures, then the optimal Target Criterion must also depend on housing prices. In the empirically realistic case where housing is subsidized and where monopoly power causes output to fall short of its optimal level, the robustly optimal Target Criterion requires the central bank to ‘lean against’ housing prices: following unexpected housing price increases, policy should adopt a stance that is projected to undershoot its normal Targets for inflation and the output gap, and similarly aim to overshoot those Targets in the case of unexpected declines in housing prices. The robustly optimal Target Criterion does not require that policy distinguish between ‘fundamental’ and ‘non-fundamental’ movements in housing prices.

  • Housing Prices and Robustly Optimal Monetary Policy
    2013
    Co-Authors: Michael Woodford, Klaus Adam
    Abstract:

    We analytically characterize robustly optimal monetary policy for an augmented New Keynesian model with a housing sector. In our setting, the housing stock delivers a service flow entering households’ utility, houses are durable goods that depreciate over time, and new houses can be produced using a concave production technology. We show that shocks to housing demand and to housing productivity have “cost-push” implications, which warrant temporary fluctuations in the inflation rate under optimal policy, even under an assumption of rational expectations, for reasons familiar from the literature on “flexible inflation Targeting”. However, under rational expectations optimal monetary policy can still be characterized by commitment to a “Target Criterion” that refers to inflation and the output gap only, just as in the standard model without a housing sector. Instead, if policy is to be robust to potential departures of (house price and inflation) expectations from model-consistent ones, the Target Criterion must also depend on housing prices. In the empirically realistic case where the government subsidizes housing, the robustly optimal Target Criterion requires the central bank to “lean against” unexpected increases in housing prices, in the sense that it should adopt a policy stance that is projected to undershoot its normal Targets for inflation and/or the output gap owing to the increase in housing prices, and similarly aim to overshoot those Targets in the case of unexpected declines in housing prices. ∗We thank Lars Hansen and Monika Piazzesi for helpful comments, and the European Research Council (Starting Grant no. 284262) and the Institute for New Economic Thinking for research support.

  • Inflation Targeting and Financial Stability
    2012
    Co-Authors: Michael Woodford
    Abstract:

    A number of commentators have argued that the desirability of inflation Targeting as a framework for monetary policy analysis should be reconsidered in light of the global financial crisis, on the ground that it requires neglect of the implications of monetary policy for financial stability. This paper argues that monetary policy may indeed affect the severity of risks to financial stability, but that it is possible to generalize an inflation Targeting framework to take account of financial stability concerns alongside traditional stabilization objectives. The resulting framework can still be viewed as a form of flexible inflation Targeting; in particular, the paper proposes a Target Criterion that would still imply an invariant long-run price level, despite fluctuations over time in risks to financial stability or even the occurrence of occasional financial crises.

  • Robustly optimal monetary policy in a microfounded New Keynesian model
    Journal of Monetary Economics, 2012
    Co-Authors: Klaus Adam, Michael Woodford
    Abstract:

    We consider optimal monetary stabilization policy in a New Keynesian model with explicit microfoundations, when the central bank recognizes that private-sector expectations need not be precisely model-consistent, and wishes to choose a policy that will be as good as possible in the case of any beliefs close enough to model-consistency. We show how to characterize robustly optimal policy without restricting consideration a priori to a particular parametric family of candidate policy rules. We show that robustly optimal policy can be implemented through commitment to a Target Criterion involving only the paths of inflation and a suitably defined output gap, but that a concern for robustness requires greater resistance to surprise increases in inflation than would be considered optimal if one could count on the private sector to have “rational expectations.”

Heikki Haario - One of the best experts on this subject based on the ideXlab platform.

  • optimization of nwp model closure parameters using total energy norm of forecast error as a Target
    Geoscientific Model Development, 2014
    Co-Authors: Pirkka Ollinaho, Marko Laine, Jouni Susiluoto, Heikki Järvinen, Peter Bauer, P Bechtold, Heikki Haario
    Abstract:

    Abstract. We explore the use of dry total energy norm in improving numerical weather prediction (NWP) model forecast skill. The Ensemble Prediction and Parameter Estimation System (EPPES) is utilized to estimate ECHAM5 atmospheric GCM (global circulation models) closure parameters related to clouds and precipitation. The Target Criterion in the optimization is the dry total energy norm of 3-day forecast error with respect to the ECMWF (European Centre for Medium-Range Weather Forecasts) operational analyses. The results are summarized as follows: (i) forecast error growth in terms of energy norm is slower in the optimized than in the default model up to day 10 forecasts (and beyond), (ii) headline forecast skill scores are improved in the training sample as well as in independent samples, (iii) the decrease of the forecast error energy norm at day three is mainly because of smaller kinetic energy error in the tropics, and (iv) this impact is spread into midlatitudes at longer ranges and appears as a smaller forecast error of potential energy. The interpretation of these results is that the parameter optimization has reduced the model error so that the forecasts remain longer in the vicinity of the analyzed state.

  • Total energy norm in NWP closure parameter optimization
    Geoscientific Model Development Discussions, 2013
    Co-Authors: Pirkka Ollinaho, Marko Laine, Jouni Susiluoto, Heikki Järvinen, Peter Bauer, P Bechtold, Heikki Haario
    Abstract:

    Abstract. We explore the use of total energy norm in improving numerical weather prediction (NWP) model forecast skill. The Ensemble Prediction and Parameter Estimation System (EPPES) is utilized to estimate ECHAM5 atmospheric GCM closure parameters related to clouds and precipitation. The Target Criterion in the optimization is the total energy norm of three-day forecast error with respect to the ECMWF operational analyses. The results are summarized as follows: (i) forecast error growth in terms of energy norm is slower in the optimized than in the default model up to day ten forecasts (and beyond), (ii) headline forecast skill scores are improved in the training sample as well as in independent samples, (iii) the decrease of the forecast error energy norm at day three is mainly because of smaller kinetic energy error in the tropics, and (iv) this impact is spread into mid-latitudes at longer ranges and appears as smaller forecast error of potential energy. The interpretation of these results is that the parameter optimization has reduced the model error so that the forecasts remain longer in the vicinity of the analyzed state.

  • Parameter variations in prediction skill optimization at ECMWF
    Nonlinear Processes in Geophysics, 2013
    Co-Authors: Pirkka Ollinaho, Marko Laine, Heikki Haario, Peter Bechtold, Martin Leutbecher, Antti Solonen, Heikki Järvinen
    Abstract:

    Abstract. Algorithmic numerical weather prediction (NWP) skill optimization has been tested using the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). We report the results of initial experimentation using importance sampling based on model parameter estimation methodology Targeted for ensemble prediction systems, called the ensemble prediction and parameter estimation system (EPPES). The same methodology was earlier proven to be a viable concept in low-order ordinary differential equation systems, and in large-scale atmospheric general circulation models (ECHAM5). Here we show that prediction skill optimization is possible even in the context of a system that is (i) of very high dimensionality, and (ii) carefully tuned to very high skill. We concentrate on four closure parameters related to the parameterizations of sub-grid scale physical processes of convection and formation of convective precipitation. We launch standard ensembles of medium-range predictions such that each member uses different values of the four parameters, and make sequential statistical inferences about the parameter values. Our Target Criterion is the squared forecast error of the 500 hPa geopotential height at day three and day ten. The EPPES methodology is able to converge towards closure parameter values that optimize the Target Criterion. Therefore, we conclude that estimation and cost function-based tuning of low-dimensional static model parameters is possible despite the very high dimensional state space, as well as the presence of stochastic noise due to initial state and physical tendency perturbations. The remaining question before EPPES can be considered as a generally applicable tool in model development is the correct formulation of the Target Criterion. The one used here is, in our view, very selective. Considering the multi-faceted question of improving forecast model performance, a more general Target Criterion should be developed. This is a topic of ongoing research.

Pirkka Ollinaho - One of the best experts on this subject based on the ideXlab platform.

  • optimization of nwp model closure parameters using total energy norm of forecast error as a Target
    Geoscientific Model Development, 2014
    Co-Authors: Pirkka Ollinaho, Marko Laine, Jouni Susiluoto, Heikki Järvinen, Peter Bauer, P Bechtold, Heikki Haario
    Abstract:

    Abstract. We explore the use of dry total energy norm in improving numerical weather prediction (NWP) model forecast skill. The Ensemble Prediction and Parameter Estimation System (EPPES) is utilized to estimate ECHAM5 atmospheric GCM (global circulation models) closure parameters related to clouds and precipitation. The Target Criterion in the optimization is the dry total energy norm of 3-day forecast error with respect to the ECMWF (European Centre for Medium-Range Weather Forecasts) operational analyses. The results are summarized as follows: (i) forecast error growth in terms of energy norm is slower in the optimized than in the default model up to day 10 forecasts (and beyond), (ii) headline forecast skill scores are improved in the training sample as well as in independent samples, (iii) the decrease of the forecast error energy norm at day three is mainly because of smaller kinetic energy error in the tropics, and (iv) this impact is spread into midlatitudes at longer ranges and appears as a smaller forecast error of potential energy. The interpretation of these results is that the parameter optimization has reduced the model error so that the forecasts remain longer in the vicinity of the analyzed state.

  • Total energy norm in NWP closure parameter optimization
    Geoscientific Model Development Discussions, 2013
    Co-Authors: Pirkka Ollinaho, Marko Laine, Jouni Susiluoto, Heikki Järvinen, Peter Bauer, P Bechtold, Heikki Haario
    Abstract:

    Abstract. We explore the use of total energy norm in improving numerical weather prediction (NWP) model forecast skill. The Ensemble Prediction and Parameter Estimation System (EPPES) is utilized to estimate ECHAM5 atmospheric GCM closure parameters related to clouds and precipitation. The Target Criterion in the optimization is the total energy norm of three-day forecast error with respect to the ECMWF operational analyses. The results are summarized as follows: (i) forecast error growth in terms of energy norm is slower in the optimized than in the default model up to day ten forecasts (and beyond), (ii) headline forecast skill scores are improved in the training sample as well as in independent samples, (iii) the decrease of the forecast error energy norm at day three is mainly because of smaller kinetic energy error in the tropics, and (iv) this impact is spread into mid-latitudes at longer ranges and appears as smaller forecast error of potential energy. The interpretation of these results is that the parameter optimization has reduced the model error so that the forecasts remain longer in the vicinity of the analyzed state.

  • Parameter variations in prediction skill optimization at ECMWF
    Nonlinear Processes in Geophysics, 2013
    Co-Authors: Pirkka Ollinaho, Marko Laine, Heikki Haario, Peter Bechtold, Martin Leutbecher, Antti Solonen, Heikki Järvinen
    Abstract:

    Abstract. Algorithmic numerical weather prediction (NWP) skill optimization has been tested using the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). We report the results of initial experimentation using importance sampling based on model parameter estimation methodology Targeted for ensemble prediction systems, called the ensemble prediction and parameter estimation system (EPPES). The same methodology was earlier proven to be a viable concept in low-order ordinary differential equation systems, and in large-scale atmospheric general circulation models (ECHAM5). Here we show that prediction skill optimization is possible even in the context of a system that is (i) of very high dimensionality, and (ii) carefully tuned to very high skill. We concentrate on four closure parameters related to the parameterizations of sub-grid scale physical processes of convection and formation of convective precipitation. We launch standard ensembles of medium-range predictions such that each member uses different values of the four parameters, and make sequential statistical inferences about the parameter values. Our Target Criterion is the squared forecast error of the 500 hPa geopotential height at day three and day ten. The EPPES methodology is able to converge towards closure parameter values that optimize the Target Criterion. Therefore, we conclude that estimation and cost function-based tuning of low-dimensional static model parameters is possible despite the very high dimensional state space, as well as the presence of stochastic noise due to initial state and physical tendency perturbations. The remaining question before EPPES can be considered as a generally applicable tool in model development is the correct formulation of the Target Criterion. The one used here is, in our view, very selective. Considering the multi-faceted question of improving forecast model performance, a more general Target Criterion should be developed. This is a topic of ongoing research.

Jouni Susiluoto - One of the best experts on this subject based on the ideXlab platform.

  • optimization of nwp model closure parameters using total energy norm of forecast error as a Target
    Geoscientific Model Development, 2014
    Co-Authors: Pirkka Ollinaho, Marko Laine, Jouni Susiluoto, Heikki Järvinen, Peter Bauer, P Bechtold, Heikki Haario
    Abstract:

    Abstract. We explore the use of dry total energy norm in improving numerical weather prediction (NWP) model forecast skill. The Ensemble Prediction and Parameter Estimation System (EPPES) is utilized to estimate ECHAM5 atmospheric GCM (global circulation models) closure parameters related to clouds and precipitation. The Target Criterion in the optimization is the dry total energy norm of 3-day forecast error with respect to the ECMWF (European Centre for Medium-Range Weather Forecasts) operational analyses. The results are summarized as follows: (i) forecast error growth in terms of energy norm is slower in the optimized than in the default model up to day 10 forecasts (and beyond), (ii) headline forecast skill scores are improved in the training sample as well as in independent samples, (iii) the decrease of the forecast error energy norm at day three is mainly because of smaller kinetic energy error in the tropics, and (iv) this impact is spread into midlatitudes at longer ranges and appears as a smaller forecast error of potential energy. The interpretation of these results is that the parameter optimization has reduced the model error so that the forecasts remain longer in the vicinity of the analyzed state.

  • Total energy norm in NWP closure parameter optimization
    Geoscientific Model Development Discussions, 2013
    Co-Authors: Pirkka Ollinaho, Marko Laine, Jouni Susiluoto, Heikki Järvinen, Peter Bauer, P Bechtold, Heikki Haario
    Abstract:

    Abstract. We explore the use of total energy norm in improving numerical weather prediction (NWP) model forecast skill. The Ensemble Prediction and Parameter Estimation System (EPPES) is utilized to estimate ECHAM5 atmospheric GCM closure parameters related to clouds and precipitation. The Target Criterion in the optimization is the total energy norm of three-day forecast error with respect to the ECMWF operational analyses. The results are summarized as follows: (i) forecast error growth in terms of energy norm is slower in the optimized than in the default model up to day ten forecasts (and beyond), (ii) headline forecast skill scores are improved in the training sample as well as in independent samples, (iii) the decrease of the forecast error energy norm at day three is mainly because of smaller kinetic energy error in the tropics, and (iv) this impact is spread into mid-latitudes at longer ranges and appears as smaller forecast error of potential energy. The interpretation of these results is that the parameter optimization has reduced the model error so that the forecasts remain longer in the vicinity of the analyzed state.

P Bechtold - One of the best experts on this subject based on the ideXlab platform.

  • optimization of nwp model closure parameters using total energy norm of forecast error as a Target
    Geoscientific Model Development, 2014
    Co-Authors: Pirkka Ollinaho, Marko Laine, Jouni Susiluoto, Heikki Järvinen, Peter Bauer, P Bechtold, Heikki Haario
    Abstract:

    Abstract. We explore the use of dry total energy norm in improving numerical weather prediction (NWP) model forecast skill. The Ensemble Prediction and Parameter Estimation System (EPPES) is utilized to estimate ECHAM5 atmospheric GCM (global circulation models) closure parameters related to clouds and precipitation. The Target Criterion in the optimization is the dry total energy norm of 3-day forecast error with respect to the ECMWF (European Centre for Medium-Range Weather Forecasts) operational analyses. The results are summarized as follows: (i) forecast error growth in terms of energy norm is slower in the optimized than in the default model up to day 10 forecasts (and beyond), (ii) headline forecast skill scores are improved in the training sample as well as in independent samples, (iii) the decrease of the forecast error energy norm at day three is mainly because of smaller kinetic energy error in the tropics, and (iv) this impact is spread into midlatitudes at longer ranges and appears as a smaller forecast error of potential energy. The interpretation of these results is that the parameter optimization has reduced the model error so that the forecasts remain longer in the vicinity of the analyzed state.

  • Total energy norm in NWP closure parameter optimization
    Geoscientific Model Development Discussions, 2013
    Co-Authors: Pirkka Ollinaho, Marko Laine, Jouni Susiluoto, Heikki Järvinen, Peter Bauer, P Bechtold, Heikki Haario
    Abstract:

    Abstract. We explore the use of total energy norm in improving numerical weather prediction (NWP) model forecast skill. The Ensemble Prediction and Parameter Estimation System (EPPES) is utilized to estimate ECHAM5 atmospheric GCM closure parameters related to clouds and precipitation. The Target Criterion in the optimization is the total energy norm of three-day forecast error with respect to the ECMWF operational analyses. The results are summarized as follows: (i) forecast error growth in terms of energy norm is slower in the optimized than in the default model up to day ten forecasts (and beyond), (ii) headline forecast skill scores are improved in the training sample as well as in independent samples, (iii) the decrease of the forecast error energy norm at day three is mainly because of smaller kinetic energy error in the tropics, and (iv) this impact is spread into mid-latitudes at longer ranges and appears as smaller forecast error of potential energy. The interpretation of these results is that the parameter optimization has reduced the model error so that the forecasts remain longer in the vicinity of the analyzed state.