scholarly journals Should Forecasters Use Real-Time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence

2019 ◽  
Vol 20 (4) ◽  
pp. e170-e200 ◽  
Author(s):  
Katja Heinisch ◽  
Rolf Scheufele
Keyword(s):  
Real Time ◽  
Optimal Combination ◽  
Financial Data ◽  
Time Data ◽  
Hard Data ◽  
Forecasting Performance ◽  
Real Time Data ◽  
Final Data ◽  
Release Data

Abstract In this paper, we investigate whether differences exist among forecasts using real-time or latest-available data to predict gross domestic product (GDP). We employ mixed-frequency models and real-time data to reassess the role of surveys and financial data relative to industrial production and orders in Germany. Although we find evidence that forecast characteristics based on real-time and final data releases differ, we also observe minimal impacts on the relative forecasting performance of indicator models. However, when obtaining the optimal combination of soft and hard data, the use of final release data may understate the role of survey information.

scholarly journals Sensors and tribological systems: applications for industry 4.0

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Saquib Rouf ◽  
Ankush Raina ◽  
Mir Irfan Ul Haq ◽  
Nida Naveed
Keyword(s):  
Real Time ◽  
Industry 4.0 ◽  
Developing Economies ◽  
Industrial Applications ◽  
Vital Role ◽  
Critical Parameters ◽  
Time Data ◽  
Content Type ◽  
Real Time Data

Purpose The involvement of wear, friction and lubrication in engineering systems and industrial applications makes it imperative to study the various aspects of tribology in relation with advanced technologies and concepts. The concept of Industry 4.0 and its implementation further faces a lot of barriers, particularly in developing economies. Real-time and reliable data is an important enabler for the implementation of the concept of Industry 4.0. For availability of reliable and real-time data about various tribological systems is crucial in applying the various concepts of Industry 4.0. This paper aims to attempt to highlight the role of sensors related to friction, wear and lubrication in implementing Industry 4.0 in various tribology-related industries and equipment. Design/methodology/approach A through literature review has been done to study the interrelationships between the availability of tribology-related data and implementation of Industry 4.0 are also discussed. Relevant and recent research papers from prominent databases have been included. A detailed overview about the various types of sensors used in generating tribological data is also presented. Some studies related to the application of machine learning and artificial intelligence (AI) are also included in the paper. A discussion on fault diagnosis and cyber physical systems in connection with tribology has also been included. Findings Industry 4.0 and tribology are interconnected through various means and the various pillars of Industry 4.0 such as big data, AI can effectively be implemented in various tribological systems. Data is an important parameter in the effective application of concepts of Industry 4.0 in the tribological environment. Sensors have a vital role to play in the implementation of Industry 4.0 in tribological systems. Determining the machine health, carrying out maintenance in off-shore and remote mechanical systems is possible by applying online-real-time data acquisition. Originality/value The paper tries to relate the pillars of Industry 4.0 with various aspects of tribology. The paper is a first of its kind wherein the interdisciplinary field of tribology has been linked with Industry 4.0. The paper also highlights the role of sensors in generating tribological data related to the critical parameters, such as wear rate, coefficient of friction, surface roughness which is critical in implementing the various pillars of Industry 4.0.

scholarly journals The Unreliability of Output-Gap Estimates in Real Time

2017 ◽  
Vol 56 (3) ◽  
pp. 193-219 ◽  
Author(s):  
Ahsan Ul Haq Satti ◽  
Wasim Shahid Malik
Keyword(s):  
Real Time ◽  
Linear Trend ◽  
The State ◽  
Output Gap ◽  
Policy Decisions ◽  
Time Data ◽  
Uncertainty Measurement ◽  
Real Time Data ◽  
Final Data ◽  
The Difference

Most research on monetary policy assumes availability of information regarding the current state of economy, at the time of the policy decision. A key challenge for policy-makers is to find indicators that give a clear and precise signal of the state of the economy in real time—that is, when policy decisions are actually taken. One of the indicators used to asses the economic condition is the output gap; and the estimates of output gap from real time data misrepresents the true state of economy. So the policy decisions taken on the basis of real time noisy data are proved wrong when true data become available. Within this context we find evidence of wrong estimates of output gap in real time data. This is done by comparing estimates of output gap based on real time data with that in the revised data. The quasi real time data are also constructed such that the difference between estimates of output gap from real time data and that from quasi real time data reflects data revision and the difference between estimates of output gap from final data and that from quasi real time data portray other revisions including end sample bias. Moreover, output gap is estimated with the help of five methods namely the linear trend method, quadratic trend method, Hordrick-Prescott (HP) filter, production function method, and structural vector autoregressive method. Results indicate that the estimates of output gap in real time data are different from what have been found in final data but other revisions, compared to data revisions, are found more significant. Moreover, the output gap measured using all the methods, except the linear trend method, appropriately portray the state of economy in the historical context. It is also found that recessions can be better predicted by real time data instead of revised data, and final data show more intensity of recession compared with what has been shown in real time data. JEL Classification: E320 Keywords: Data Uncertainty, Measurement Uncertainty, Output Gap, Business Cycle, Economic Activity

scholarly journals DATA REVISIONS IN THE ESTIMATION OF DSGE MODELS

2016 ◽  
Vol 20 (7) ◽  
pp. 1683-1716 ◽  
Author(s):  
Miguel Casares ◽  
Jesús Vázquez
Keyword(s):  
Interest Rates ◽  
Real Time ◽  
High Variability ◽  
Dsge Models ◽  
Time Data ◽  
Time Consumption ◽  
Data Revisions ◽  
The Standard Model ◽  
Real Time Data

Revisions of U.S. macroeconomic data are persistent, correlated with real-time data, and with high variability (around 80% of U.S. real-time data volatility). This paper adapts a DSGE-style model to accommodate both real-time and revised data from the U.S. economy. The results show a lesser role of both habit formation and price indexation than in the standard model. In the simulations, revision shocks to both output and inflation are expansionary because the Fed reacts by cutting interest rates. Consumption revisions, in contrast, are countercyclical, consumption mirrors the observed reduction in real-time consumption. In the variance decomposition, data revisions explain 9.3% of output changes.

Real-time probability forecasts of UK macroeconomic events

2008 ◽  
Vol 203 ◽  
pp. 78-90
Author(s):  
Anthony Garratt ◽  
Kevin Lee ◽  
Shaun Vahey
Keyword(s):  
Real Time ◽  
Aggregate Demand ◽  
Time Data ◽  
Probability Forecast ◽  
The Past ◽  
Data Revisions ◽  
Real Time Data ◽  
Probability Forecasts ◽  
Density Forecasts ◽  
Release Data

An overview is provided of the issues raised in the recent literature on the use of real-time data in the context of nowcasting and forecasting UK macroeconomic events. The ideas are illustrated through two specific applications using UK real-time data available over 1961-2006 and providing probability forecasts that could have been produced in real time over the past twenty years. In the first, we consider the reliability of first-release data on the components of UK aggregate demand by looking at forecasts of the probability of substantial data revisions. In the second, we consider the estimation of the output gap, illustrating the uncertainty surrounding its measurement through density forecasts and focusing on its interpretation in terms of inflationary pressure through an event probability forecast.

scholarly journals Design of Automized Inspection Vehicle for RTO

2020 ◽  
Vol 9 (5) ◽  
pp. 689-692
Keyword(s):  
Real Time ◽  
Current Trend ◽  
Road Accidents ◽  
Time Data ◽  
The People ◽  
Real Time Data ◽  
Chain Of Trust ◽  
Embedded Programming ◽  
Electronic Sensors

As we are all aware of current trend of Regional Traffic Office (RTO) system. One has to put a lot of effort in order to get licences from RTO. It is very tedious as well as time consuming process. Otherwise in order to get licences quickly we used to get the help of private agents by paying them much more money which is actually an illegal act according the rules of our government. By which people have to suffer a lot. So, with the help of technology we can convert the present tedious and time wasting process of RTO system into simpler and fruitful one. By this way our project also helps us to reduce the corruption which is present in today’s RTO system by completely removing the role of private agents and the people also automatically get benefited with this new RTO system”. The main aim of designed system in paper is to minimize the corruption by suggesting an ideal module which can be implemented in number of places for number of operation. minimize risk of internal fraud and enhance efficiencies by automating process and minimizing operator intervention, through this we can create an integrated , chain of trust for the identity and issuance process. Creating a secure chain of trust using electronic sensors, microcontroller and embedded programming. This System can be connected to IOT device and we can monitor real time data. Designed system is capable of removing corruption occurring while granting a licence to an applicant, and also capable of avoiding on road accidents caused due to inappropriate driving, because in the system licence will give to the applicant which can drive the vehicle properly.

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
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