Author: Thomas C. Stark
Publisher:
ISBN:
Category : Economic forecasting
Languages : en
Pages : 46

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Book Description

Author: Tom Stark
Publisher:
ISBN:
Category :
Languages : en
Pages : 46

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Book Description

Author: Peter Fuleky
Publisher: Springer Nature
ISBN: 3030311503
Category : Business & Economics
Languages : en
Pages : 716

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Book Description
This book surveys big data tools used in macroeconomic forecasting and addresses related econometric issues, including how to capture dynamic relationships among variables; how to select parsimonious models; how to deal with model uncertainty, instability, non-stationarity, and mixed frequency data; and how to evaluate forecasts, among others. Each chapter is self-contained with references, and provides solid background information, while also reviewing the latest advances in the field. Accordingly, the book offers a valuable resource for researchers, professional forecasters, and students of quantitative economics.

Author: Dean Darrell Croushore
Publisher:
ISBN:
Category : Economic forecasting
Languages : en
Pages : 24

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Book Description

Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Book Description
The Federal Reserve Bank of Philadelphia presents the full text of the June 1999 working paper entitled "A Real-time Data Set for Macroeconomists," written by Dean Croushore and Tom Stark. The text is available in PDF format. This paper features the concept and uses of a real-time data set that can be used by economists for testing published econometric results, for analyzing policy, and for forecasting. The data set consists of the major macroeconomic data available at quarterly intervals in real time.

Author: Dean CROUSHORE
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Book Description

Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Book Description
The Federal Reserve Bank of Philadelphia presents the full text of the June 2000 working paper entitled "A Real-time Data Set for Macroeconomists: Does Data Vintage Matter for Forecasting?," written by Dean Croushore and Tom Stark. The text is available in PDF format. This paper features a real-time data set for macroeconomists that can be used for a variety of purposes, including forecast evaluation. The authors describe the construction of the data set and the properties of the variables across vintages, and provide examples showing how data revisions can affect forecasts.

Author: Dean Croushore
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Book Description
This paper presents the concept and uses of a real-time data set that can be used by economists for testing the robustness of published econometric results, for analyzing policy, and for forecasting. The data set consists of vintages, or snapshots, of the major macroeconomic data available at quarterly intervals in real time. The paper illustrates why such data may matter, explains the construction of the data set, examines the properties of several of the variables in the data set across vintages, examines key empirical papers in macroeconomics and investigates their robustness to different vintages, looks at how policy analysis may be affected by data revisions, and shows how forecasts can be affected by data revisions.

Author: Dean Darrell Croushore
Publisher:
ISBN:
Category : Economic forecasting
Languages : en
Pages : 47

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Book Description

Author: Sergio Consoli
Publisher: Springer Nature
ISBN: 3030668916
Category : Application software
Languages : en
Pages : 357

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Book Description
This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.