Reliable real-time estimates of the euro-area output gap

We study the properties of the IMF-WEO estimates of real-time output gaps for countries in the euro area as well as the determinants of their revisions over 1994-2017. The analysis shows that staff typically saw economies as operating below their potential. In real time, output gaps tend to have large and negative averages that are largely revised away in later vintages. Most of the mis-measurement in real time can be explained by the difficulty in predicting recessions and by overestimation of the economy’s potential capacity. We also find, in line with earlier literature, that real-time output gaps are not useful for predicting inflation. In addition, countries where slack (and potential growth) is overestimated to a larger extent primary fiscal balances tend to be lower and public debt ratios are higher and increase faster than projected. Previous research suggests that national authorities’ real-time output gaps suffer from a similar bias. To the extent these estimates play a role in calibrating fiscal policy, over-optimism about long-term growth could contribute to excessive deficits and debt buildup.

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TREND-CYCLE DECOMPOSITION OF OUTPUT AND EURO AREA INFLATION FORECASTS: A REAL-TIME APPROACH BASED ON MODEL COMBINATION

Macroeconomic Dynamics ◽

10.1017/s1365100513000461 ◽

2013 ◽

Vol 19 (2) ◽

pp. 363-393 ◽

Cited By ~ 3

Author(s):

Pierre Guérin ◽

Laurent Maurin ◽

Matthias Mohr

Keyword(s):

Real Time ◽

Euro Area ◽

Output Gap ◽

Model Combination ◽

Cycle Decomposition ◽

Potential Output ◽

Inflation Forecasts ◽

Data Revisions ◽

Ex Post ◽

Decomposition Models

This paper estimates univariate and multivariate trend-cycle decomposition models of GDP and considers the novel possibility of regime switches in the growth of potential output. We compute both ex post and real-time estimates of the output gap to check the stability of our estimates to GDP data revisions. We find some evidence of regime changes in the growth of potential output during the recessions experienced by the euro area. We also run a forecasting experiment to evaluate the predictive power of the output gap for inflation. The benchmark autoregressive model tends to obtain the best forecasts for one-quarter-ahead forecasts, but the output gap measures help to forecast inflation for longer horizons.

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Real-Time Musculoskeletal Kinematics and Dynamics Analysis Using Marker- and IMU-Based Solutions in Rehabilitation

Sensors ◽

10.3390/s21051804 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1804

Author(s):

Dimitar Stanev ◽

Konstantinos Filip ◽

Dimitrios Bitzas ◽

Sokratis Zouras ◽

Georgios Giarmatzis ◽

...

Keyword(s):

Real Time ◽

Small Error ◽

Grand Challenge ◽

Muscle Forces ◽

Time Estimates ◽

Musculoskeletal Models ◽

Open Source Framework ◽

Measurement Units ◽

Technical Details ◽

Significant Milestone

This study aims to explore the possibility of estimating a multitude of kinematic and dynamic quantities using subject-specific musculoskeletal models in real-time. The framework was designed to operate with marker-based and inertial measurement units enabling extensions far beyond dedicated motion capture laboratories. We present the technical details for calculating the kinematics, generalized forces, muscle forces, joint reaction loads, and predicting ground reaction wrenches during walking. Emphasis was given to reduce computational latency while maintaining accuracy as compared to the offline counterpart. Notably, we highlight the influence of adequate filtering and differentiation under noisy conditions and its importance for consequent dynamic calculations. Real-time estimates of the joint moments, muscle forces, and reaction loads closely resemble OpenSim’s offline analyses. Model-based estimation of ground reaction wrenches demonstrates that even a small error can negatively affect other estimated quantities. An application of the developed system is demonstrated in the context of rehabilitation and gait retraining. We expect that such a system will find numerous applications in laboratory settings and outdoor conditions with the advent of predicting or sensing environment interactions. Therefore, we hope that this open-source framework will be a significant milestone for solving this grand challenge.

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