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Author: Sercan Eraslan
Publisher:
ISBN: 9783957296405
Category :
Languages : de
Pages :
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Author: Sercan Eraslan
Publisher:
ISBN: 9783957296405
Category :
Languages : de
Pages :
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Book Description
Author: Mr. Jean-Francois Dauphin
Publisher: International Monetary Fund
ISBN:
Category : Business & Economics
Languages : en
Pages : 45
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Book Description
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.
Author:
Publisher:
ISBN: 9789523230491
Category :
Languages : en
Pages : 41
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Book Description
Author: Christian Schumacher
Publisher:
ISBN:
Category :
Languages : en
Pages : 60
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Book Description
This paper discusses a factor model for estimating monthly GDP using a large number of monthly and quarterly time series in real-time. To take into account the different periodicities of the data and missing observations at the end of the sample, the factors are estimated by applying an EM algorithm combined with a principal components estimator. We discuss the in-sample properties of the estimator in real-time environments and methods for out-of-sample forecasting. As an empirical application, we estimate monthly German GDP in real-time, discuss the nowcast and forecast accuracy of the model and the role of revisions. Furthermore, we assess the contribution of timely monthly data to the forecast performance.
Author: Michael P. Clements
Publisher: OUP USA
ISBN: 0195398645
Category : Business & Economics
Languages : en
Pages : 732
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Book Description
Greater data availability has been coupled with developments in statistical theory and economic theory to allow more elaborate and complicated models to be entertained. These include factor models, DSGE models, restricted vector autoregressions, and non-linear models.
Author: Massimiliano Marcellino
Publisher:
ISBN: 9783865583697
Category :
Languages : de
Pages : 50
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Book Description
This paper compares different ways to estimate the current state of the economy using factor models that can handle unbalanced datasets. Due to the different release lags ofbusiness cycle indicators, data unbalancedness often emerges at the end of multivariate samples, which is sometimes referred to as the "ragged edge" of the data. Using a large monthly dataset of the German economy, we compare the performance of different factor models in the presence of the ragged edge: static and dynamic principal components based on realigned data, the Expectation-Maximisation (EM)algorithm and the Kalman smoother in a state-space model context. The monthly factors are used to estimate current quarter GDP, called the "nowcast", using different versions of whatwe call factor-based mixed-data sampling (Factor-MIDAS) approaches. We compare all possible combinations of factor estimation methods and Factor-MIDAS projections with respect to nowcast performance. Additionally, we compare the performance of the nowcast factor models with the performance of quarterly factor models based on time-aggregated and thus balanced data, which neglect the most timely observations of business cycle indicators at the end of the sample. Our empirical findings show that the factor estimation methods don't differ much with respect to nowcasting accuracy. Concerning the projections, the most parsimonious MIDAS projection performs best overall. Finally, quarterly models are in general outperformed by the nowcast factor models that can exploit ragged-edge data.
Author: Oguzhan Cepni
Publisher:
ISBN:
Category :
Languages : en
Pages : 10
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Book Description
A number of recent studies in the macro-finance literature that addresses the link between asset prices and economic fluctuations have focused on the usefulness of various factor models in the context of now-casting using very big dataset. The issue of factor extraction is usually swept under the carpet in the factor model literature, where it seems that all that is needed is a large number of economic and financial variables. We contribute to this literature by analyzing whether factor estimation methods matters for the performance of factor-based now-casting models based on selected emerging markets GDP. Ancillary findings based on our GDP now-casting experiments on major emerging market countries underscore the advantage of sparse principal component analysis based factor estimation approach. These results show that imposing a sparse structure on the whole dataset is generally a useful step towards reducing the forecast errors in the context of GDP now-casting model specification.
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.
Author: Pablo Duarte
Publisher:
ISBN:
Category :
Languages : en
Pages : 18
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Book Description
Author: Jörg Breitung
Publisher:
ISBN:
Category :
Languages : en
Pages : 40
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Book Description
Factor models can cope with many variables without running into scarce degrees of freedom.
