CW
C. Wieles
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Achieving price stability in an uncertain world
Assessing uncertainty in DSGE models
The central bank plays a crucial role in maintaining price stability, as high inflation or deflation can lead to economic instability. One of its main tools is the adjustment of interest rates to keep inflation within a desired range, particularly in response to economic shocks. To determine optimal interest rate policies, central banks often use Dynamic Stochastic General Equilibrium (DSGE) models, which simulate the behavior of consumers, firms, and the central bank while simplifying the economy to two key variables: inflation and the output gap. The output gap represents the difference between actual and potential output, while inflation measures the change in prices over time. These models incorporate economic features such as price stickiness, expectations, consumer substitution between goods, and the effects of specific shocks.
Interest rate decisions are influenced by three components: the previous period’s interest rate, current inflation, and the current output gap. While DSGE models help identify effective policies, uncertainties regarding model parameters and shock characteristics can limit their accuracy. To address this, this research applies Exploratory Modeling and Analysis (EMA), which involves running large numbers of experiments across a range of parameter values to observe how different assumptions affect economic outcomes. EMA enables robust policy design by examining the effects of parametric and shock uncertainties on model predictions.
Experiments revealed that the most influential parameters in response to a positive demand shock are the share of firms able to adjust prices, the central bank’s conservativeness in adjusting interest rates, and the persistence of the shock. Large deviations and oscillations in inflation and output gap occur when a high share of firms can adjust prices while the central bank reacts conservatively. In contrast, when price stickiness is high, inflation remains moderate but the output gap is positive, indicating stronger demand relative to supply. Negative supply shocks primarily affect the magnitude of deviations rather than creating distinct behavioral patterns.
EMA also allows for behavior-based scenario discovery using clustering techniques to identify parameter combinations associated with extreme or undesirable economic behavior. This is complemented by multi-objective optimization, which seeks policies that minimize deviations and fluctuations in inflation and output gap while keeping inflation within acceptable limits. Pareto optimal policies are evaluated for robustness across a wide range of uncertain economic states. The results indicate that policies which prioritize responsiveness to current inflation and reduce the influence of past interest rates perform best in controlling inflation, although they may lead to larger deviations in output gap.
Overall, EMA provides significant added value to DSGE modeling by revealing which parameters are most critical to economic behavior, highlighting trade-offs between policy objectives, and offering methods to design robust policies under uncertainty. While DSGE models simplify the economy and cannot capture all real-world dynamics, combining them with EMA enables central banks to better understand risks, assess alternative policies, and design strategies that are resilient to both parametric and shock uncertainties. Future research could apply EMA to more detailed DSGE models or real-world data, while ensuring validity and robustness in practical policy contexts. ...
Interest rate decisions are influenced by three components: the previous period’s interest rate, current inflation, and the current output gap. While DSGE models help identify effective policies, uncertainties regarding model parameters and shock characteristics can limit their accuracy. To address this, this research applies Exploratory Modeling and Analysis (EMA), which involves running large numbers of experiments across a range of parameter values to observe how different assumptions affect economic outcomes. EMA enables robust policy design by examining the effects of parametric and shock uncertainties on model predictions.
Experiments revealed that the most influential parameters in response to a positive demand shock are the share of firms able to adjust prices, the central bank’s conservativeness in adjusting interest rates, and the persistence of the shock. Large deviations and oscillations in inflation and output gap occur when a high share of firms can adjust prices while the central bank reacts conservatively. In contrast, when price stickiness is high, inflation remains moderate but the output gap is positive, indicating stronger demand relative to supply. Negative supply shocks primarily affect the magnitude of deviations rather than creating distinct behavioral patterns.
EMA also allows for behavior-based scenario discovery using clustering techniques to identify parameter combinations associated with extreme or undesirable economic behavior. This is complemented by multi-objective optimization, which seeks policies that minimize deviations and fluctuations in inflation and output gap while keeping inflation within acceptable limits. Pareto optimal policies are evaluated for robustness across a wide range of uncertain economic states. The results indicate that policies which prioritize responsiveness to current inflation and reduce the influence of past interest rates perform best in controlling inflation, although they may lead to larger deviations in output gap.
Overall, EMA provides significant added value to DSGE modeling by revealing which parameters are most critical to economic behavior, highlighting trade-offs between policy objectives, and offering methods to design robust policies under uncertainty. While DSGE models simplify the economy and cannot capture all real-world dynamics, combining them with EMA enables central banks to better understand risks, assess alternative policies, and design strategies that are resilient to both parametric and shock uncertainties. Future research could apply EMA to more detailed DSGE models or real-world data, while ensuring validity and robustness in practical policy contexts. ...
The central bank plays a crucial role in maintaining price stability, as high inflation or deflation can lead to economic instability. One of its main tools is the adjustment of interest rates to keep inflation within a desired range, particularly in response to economic shocks. To determine optimal interest rate policies, central banks often use Dynamic Stochastic General Equilibrium (DSGE) models, which simulate the behavior of consumers, firms, and the central bank while simplifying the economy to two key variables: inflation and the output gap. The output gap represents the difference between actual and potential output, while inflation measures the change in prices over time. These models incorporate economic features such as price stickiness, expectations, consumer substitution between goods, and the effects of specific shocks.
Interest rate decisions are influenced by three components: the previous period’s interest rate, current inflation, and the current output gap. While DSGE models help identify effective policies, uncertainties regarding model parameters and shock characteristics can limit their accuracy. To address this, this research applies Exploratory Modeling and Analysis (EMA), which involves running large numbers of experiments across a range of parameter values to observe how different assumptions affect economic outcomes. EMA enables robust policy design by examining the effects of parametric and shock uncertainties on model predictions.
Experiments revealed that the most influential parameters in response to a positive demand shock are the share of firms able to adjust prices, the central bank’s conservativeness in adjusting interest rates, and the persistence of the shock. Large deviations and oscillations in inflation and output gap occur when a high share of firms can adjust prices while the central bank reacts conservatively. In contrast, when price stickiness is high, inflation remains moderate but the output gap is positive, indicating stronger demand relative to supply. Negative supply shocks primarily affect the magnitude of deviations rather than creating distinct behavioral patterns.
EMA also allows for behavior-based scenario discovery using clustering techniques to identify parameter combinations associated with extreme or undesirable economic behavior. This is complemented by multi-objective optimization, which seeks policies that minimize deviations and fluctuations in inflation and output gap while keeping inflation within acceptable limits. Pareto optimal policies are evaluated for robustness across a wide range of uncertain economic states. The results indicate that policies which prioritize responsiveness to current inflation and reduce the influence of past interest rates perform best in controlling inflation, although they may lead to larger deviations in output gap.
Overall, EMA provides significant added value to DSGE modeling by revealing which parameters are most critical to economic behavior, highlighting trade-offs between policy objectives, and offering methods to design robust policies under uncertainty. While DSGE models simplify the economy and cannot capture all real-world dynamics, combining them with EMA enables central banks to better understand risks, assess alternative policies, and design strategies that are resilient to both parametric and shock uncertainties. Future research could apply EMA to more detailed DSGE models or real-world data, while ensuring validity and robustness in practical policy contexts.
Interest rate decisions are influenced by three components: the previous period’s interest rate, current inflation, and the current output gap. While DSGE models help identify effective policies, uncertainties regarding model parameters and shock characteristics can limit their accuracy. To address this, this research applies Exploratory Modeling and Analysis (EMA), which involves running large numbers of experiments across a range of parameter values to observe how different assumptions affect economic outcomes. EMA enables robust policy design by examining the effects of parametric and shock uncertainties on model predictions.
Experiments revealed that the most influential parameters in response to a positive demand shock are the share of firms able to adjust prices, the central bank’s conservativeness in adjusting interest rates, and the persistence of the shock. Large deviations and oscillations in inflation and output gap occur when a high share of firms can adjust prices while the central bank reacts conservatively. In contrast, when price stickiness is high, inflation remains moderate but the output gap is positive, indicating stronger demand relative to supply. Negative supply shocks primarily affect the magnitude of deviations rather than creating distinct behavioral patterns.
EMA also allows for behavior-based scenario discovery using clustering techniques to identify parameter combinations associated with extreme or undesirable economic behavior. This is complemented by multi-objective optimization, which seeks policies that minimize deviations and fluctuations in inflation and output gap while keeping inflation within acceptable limits. Pareto optimal policies are evaluated for robustness across a wide range of uncertain economic states. The results indicate that policies which prioritize responsiveness to current inflation and reduce the influence of past interest rates perform best in controlling inflation, although they may lead to larger deviations in output gap.
Overall, EMA provides significant added value to DSGE modeling by revealing which parameters are most critical to economic behavior, highlighting trade-offs between policy objectives, and offering methods to design robust policies under uncertainty. While DSGE models simplify the economy and cannot capture all real-world dynamics, combining them with EMA enables central banks to better understand risks, assess alternative policies, and design strategies that are resilient to both parametric and shock uncertainties. Future research could apply EMA to more detailed DSGE models or real-world data, while ensuring validity and robustness in practical policy contexts.