scholarly journals Does the ECB Rely on a Taylor Rule?: Comparing Ex-Post with Real Time Data

2009 ◽  
Author(s):  
Ansgar Hubertus Belke ◽  
Jens Klose
Keyword(s):  
Real Time ◽  
Taylor Rule ◽  
Time Data ◽  
Ex Post ◽  
Real Time Data

THE CYCLICALITY OF AUTOMATIC AND DISCRETIONARY FISCAL POLICY: WHAT CAN REAL-TIME DATA TELL US?

2013 ◽  
Vol 19 (1) ◽  
pp. 221-243 ◽  
Author(s):  
Kerstin Bernoth ◽  
Andrew Hughes Hallett ◽  
John Lewis
Keyword(s):  
Fiscal Policy ◽  
Real Time ◽  
Time Data ◽  
Discretionary Fiscal Policy ◽  
Ex Post ◽  
Real Time Data ◽  
True State ◽  
State Of The Economy ◽  
Structural Budget ◽  
Automatic Stabilizers

This paper develops a new methodology for estimating both the automatic and discretionary components of fiscal policy in one reaction function using the differences between real-time and ex post data. Discretionary policy should respond to information available to the policy maker at the time (real-time data), whereas automatic fiscal policy should respond to the true state of the economy at the time (proxied by the final data). We find that the intended discretionary response of fiscal policy to the cycle is counter-cyclical. Our estimates suggest that the automatic stabilizers are at the lower end of the range found in the related literature. This new methodology reduces the risk present in the conventional CAB approach that part of the discretionary actions may be wrongly attributed to automatic stabilizers. In that sense, automatic stabilizers are typically not as strong as usually claimed. This could be of particular use in countries where insufficient data exist to estimate structural budget sensitivities directly.

399-P: Nurse-Driven Intervention Using Real-Time Data to Improve Hypoglycemia Rates in a Respiratory Care Unit

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 399-P
Author(s):  
ANN MARIE HASSE ◽  
RIFKA SCHULMAN ◽  
TORI CALDER
Keyword(s):  
Real Time ◽  
Respiratory Care ◽  
Time Data ◽  
Real Time Data

MHA releases largest real‐time data from MH help‐seekers

2021 ◽  
Vol 31 (6) ◽  
pp. 7-7
Author(s):  
Valerie A. Canady
Keyword(s):  
Real Time ◽  
Time Data ◽  
Real Time Data

Why Ecological Momentary Assessment Surveys Go Incomplete?: When It Happens and How It Impacts Data

2020 ◽  
Author(s):  
Yu-Hsiang Wu ◽  
Jingjing Xu ◽  
Elizabeth Stangl ◽  
Shareka Pentony ◽  
Dhruv Vyas ◽  
...  
Keyword(s):  
Real Time ◽  
Self Report ◽  
Data Logging ◽  
Time Data ◽  
Real Time Data ◽  
Report Data ◽  
Self Report Data ◽  
Ecological Momentary ◽  
The Impact ◽  
Momentary Assessment

Abstract Background Ecological momentary assessment (EMA) often requires respondents to complete surveys in the moment to report real-time experiences. Because EMA may seem disruptive or intrusive, respondents may not complete surveys as directed in certain circumstances. Purpose This article aims to determine the effect of environmental characteristics on the likelihood of instances where respondents do not complete EMA surveys (referred to as survey incompletion), and to estimate the impact of survey incompletion on EMA self-report data. Research Design An observational study. Study Sample Ten adults hearing aid (HA) users. Data Collection and Analysis Experienced, bilateral HA users were recruited and fit with study HAs. The study HAs were equipped with real-time data loggers, an algorithm that logged the data generated by HAs (e.g., overall sound level, environment classification, and feature status including microphone mode and amount of gain reduction). The study HAs were also connected via Bluetooth to a smartphone app, which collected the real-time data logging data as well as presented the participants with EMA surveys about their listening environments and experiences. The participants were sent out to wear the HAs and complete surveys for 1 week. Real-time data logging was triggered when participants completed surveys and when participants ignored or snoozed surveys. Data logging data were used to estimate the effect of environmental characteristics on the likelihood of survey incompletion, and to predict participants' responses to survey questions in the instances of survey incompletion. Results Across the 10 participants, 715 surveys were completed and survey incompletion occurred 228 times. Mixed effects logistic regression models indicated that survey incompletion was more likely to happen in the environments that were less quiet and contained more speech, noise, and machine sounds, and in the environments wherein directional microphones and noise reduction algorithms were enabled. The results of survey response prediction further indicated that the participants could have reported more challenging environments and more listening difficulty in the instances of survey incompletion. However, the difference in the distribution of survey responses between the observed responses and the combined observed and predicted responses was small. Conclusion The present study indicates that EMA survey incompletion occurs systematically. Although survey incompletion could bias EMA self-report data, the impact is likely to be small.

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