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Nowcasting Gdp A Scalable Approach Using Dfm Machine Learning And Novel Data Applied To European Economies
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Book Synopsis Nowcasting GDP - A Scalable Approach Using DFM, Machine Learning and Novel Data, Applied to European Economies by : Mr. Jean-Francois Dauphin
Download or read book Nowcasting GDP - A Scalable Approach Using DFM, Machine Learning and Novel Data, Applied to European Economies written by Mr. Jean-Francois Dauphin and published by International Monetary Fund. This book was released on 2022-03-11 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper describes recent work to strengthen nowcasting capacity at the IMF’s European department. It motivates and compiles datasets of standard and nontraditional variables, such as Google search and air quality. It applies standard dynamic factor models (DFMs) and several machine learning (ML) algorithms to nowcast GDP growth across a heterogenous group of European economies during normal and crisis times. Most of our methods significantly outperform the AR(1) benchmark model. Our DFMs tend to perform better during normal times while many of the ML methods we used performed strongly at identifying turning points. Our approach is easily applicable to other countries, subject to data availability.
Download or read book Nowcasting GDP written by and published by . This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Seeing in the Dark by : Mr.Andrew Tiffin
Download or read book Seeing in the Dark written by Mr.Andrew Tiffin and published by International Monetary Fund. This book was released on 2016-03-08 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: Macroeconomic analysis in Lebanon presents a distinct challenge. For example, long delays in the publication of GDP data mean that our analysis often relies on proxy variables, and resembles an extended version of the “nowcasting” challenge familiar to many central banks. Addressing this problem—and mindful of the pitfalls of extracting information from a large number of correlated proxies—we explore some recent techniques from the machine learning literature. We focus on two popular techniques (Elastic Net regression and Random Forests) and provide an estimation procedure that is intuitively familiar and well suited to the challenging features of Lebanon’s data.
Author : Publisher :International Monetary Fund ISBN 13 : Total Pages :26 pages Book Rating :4.4/5 (2 download)
Download or read book written by and published by International Monetary Fund. This book was released on with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Computational Statistical Methodologies and Modeling for Artificial Intelligence by : Priyanka Harjule
Download or read book Computational Statistical Methodologies and Modeling for Artificial Intelligence written by Priyanka Harjule and published by CRC Press. This book was released on 2023-03-31 with total page 389 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers computational statistics-based approaches for Artificial Intelligence. The aim of this book is to provide comprehensive coverage of the fundamentals through the applications of the different kinds of mathematical modelling and statistical techniques and describing their applications in different Artificial Intelligence systems. The primary users of this book will include researchers, academicians, postgraduate students, and specialists in the areas of data science, mathematical modelling, and Artificial Intelligence. It will also serve as a valuable resource for many others in the fields of electrical, computer, and optical engineering. The key features of this book are: Presents development of several real-world problem applications and experimental research in the field of computational statistics and mathematical modelling for Artificial Intelligence Examines the evolution of fundamental research into industrialized research and the transformation of applied investigation into real-time applications Examines the applications involving analytical and statistical solutions, and provides foundational and advanced concepts for beginners and industry professionals Provides a dynamic perspective to the concept of computational statistics for analysis of data and applications in intelligent systems with an objective of ensuring sustainability issues for ease of different stakeholders in various fields Integrates recent methodologies and challenges by employing mathematical modeling and statistical techniques for Artificial Intelligence
Book Synopsis Seeing in the Dark by : Andre Tiffin
Download or read book Seeing in the Dark written by Andre Tiffin and published by . This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book GDPNow written by Patrick Higgins and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Nowcasting GDP Using Machine Learning Methods by : Dennis Kant
Download or read book Nowcasting GDP Using Machine Learning Methods written by Dennis Kant and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Nowcasting and Forecasting GDP in Emerging Markets Using Global Financial and Macroeconomic Diffusion Indexes by : Oguzhan Cepni
Download or read book Nowcasting and Forecasting GDP in Emerging Markets Using Global Financial and Macroeconomic Diffusion Indexes written by Oguzhan Cepni and published by . This book was released on 2018 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, we contribute to the nascent literature on nowcasting and forecasting GDP in emerging market economies using big data methods. This is done by analyzing the usefulness of various dimension reduction, machine learning and shrinkage methods including sparse principal component analysis (SPCA), the elastic net, the least absolute shrinkage operator, and least angle regression when constructing predictions using latent global macroeconomic and financial factors (diffusion indexes) in a dynamic factor model (DFM). We also utilize a judgmental dimension reduction method called the Bloomberg Relevance Index (BBG), which is an index that assigns a measure of importance to each variable in a dataset depending on the variable's usage by market participants. In our empirical analysis, we show that DFMs, when specified using dimension reduction methods (particularly BBG and SPCA), yield superior predictions, relative to benchmark linear econometric or simple DFMs. Moreover, global financial and macroeconomic (business cycle) diffusion indexes constructed using targeted predictors are found to be important in four of the five emerging market economies (including Brazil, Mexico, South Africa, and Turkey) that we study. These findings point to the importance of spillover effects across emerging market economies, and underscore the importance of parsimoniously characterizing such linkages when utilizing high dimensional global datasets.
Book Synopsis When are Google Data Useful to Nowcast GDP? by : Laurent Ferrara
Download or read book When are Google Data Useful to Nowcast GDP? written by Laurent Ferrara and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis The Status of GDP Compilation Practices in 189 Economies and the Relevance for Policy Analysis by : Francien Berry
Download or read book The Status of GDP Compilation Practices in 189 Economies and the Relevance for Policy Analysis written by Francien Berry and published by International Monetary Fund. This book was released on 2018-03-02 with total page 19 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper examines the status of GDP compilation in 189 economies against six key criteria that describe national accounts compilation practices: whether the benchmark year is up to date, the availability and timeliness of annual and quarterly GDP, whether GDP by production and expenditure approaches are compiled independently to allow for comparisons, whether estimates by the income approach are available, and the vintage of the System of National Accounts (SNA) applied. We used publicly available information including from the IMF’s Dissemination Standards Bulletin Board (DSBB), and, for 108 developing economies, information provided by the IMF’s real sector advisors stationed in the Fund’s 10 Regional Technical Assistance Centers (RTACs). The data were compared with the UNSD and World Bank databases. We find that 50 percent of economies have acceptable benchmark years, 72 percent report timely annual GDP data, while 55 percent of economies report timely data for quarterly GDP. The study presents some conclusions for priorities of capacity development.
Book Synopsis Nowcasting GDP Using Machine Learning Algorithms by : Adam Richardson
Download or read book Nowcasting GDP Using Machine Learning Algorithms written by Adam Richardson and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Machine Learning Use in GDP Nowcasting by :
Download or read book Machine Learning Use in GDP Nowcasting written by and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Nowcasting New Zealand GDP Using Machine Learning Algorithms by : Adam Richardson
Download or read book Nowcasting New Zealand GDP Using Machine Learning Algorithms written by Adam Richardson and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Nowcasting Real GDP Growth by : Evžen Kočenda
Download or read book Nowcasting Real GDP Growth written by Evžen Kočenda and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We analyze the performance of a broad range of nowcasting and short-term forecasting models for a representative set of twelve old and six new member countries of the European Union (EU) that are characterized by substantial differences in aggregate output variability. In our analysis, we generate ex-post out-of-sample nowcasts and forecasts based on hard and soft indicators that come from a comparable set of identical data. We show that nowcasting works well for the new EU countries because, although that variability in their GDP growth data is larger than that of the old EU economies, the economic significance of nowcasting is on average somewhat larger.
Book Synopsis Nowcasting GDP Using Machine Learning Algorithms by : Adam Richardson (Economist)
Download or read book Nowcasting GDP Using Machine Learning Algorithms written by Adam Richardson (Economist) and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Forecasting and Nowcasting Emerging Market GDP Growth Rates by : Oguzhan Cepni
Download or read book Forecasting and Nowcasting Emerging Market GDP Growth Rates written by Oguzhan Cepni and published by . This book was released on 2019 with total page 47 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, we assess the predictive content of latent economic policy uncertainty and data surprises factors for forecasting and nowcasting GDP using factor-type econometric models. Our analysis focuses on five emerging market economies, including Brazil, Indonesia, Mexico, South Africa, and Turkey; and we carry out a forecasting horse-race in which predictions from various different models are compared. These models may (or may not) contain latent uncertainty and surprise factors constructed using both local and global economic datasets. The set of models that we examine in our experiments includes both simple benchmark linear econometric models as well as dynamic factor models (DFMs) that are estimated using a variety of frequentist and Bayesian data shrinkage methods based on the least absolute shrinkage operator (LASSO). We find that the inclusion of our new uncertainty and surprise factors leads to superior predictions of GDP growth, particularly when these latent factors are constructed using Bayesian variants of the LASSO. Overall, our findings point to the importance of spillover effects from global uncertainty and data surprises, when predicting GDP growth in emerging market economies.
