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Predictive Econometrics and Big Data: Studies in Computational Intelligence, cartea 753

Editat de Vladik Kreinovich, Songsak Sriboonchitta, Nopasit Chakpitak
en Limba Engleză Hardback – 2 dec 2017
This book presents recent research on predictive econometrics and big data. Gathering edited papers presented at the 11th International Conference of the Thailand Econometric Society (TES2018), held in Chiang Mai, Thailand, on January 10-12, 2018, its main focus is on predictive techniques – which directly aim at predicting economic phenomena; and big data techniques – which enable us to handle the enormous amounts of data generated by modern computers in a reasonable time. The book also discusses the applications of more traditional statistical techniques to econometric problems.
Econometrics is a branch of economics that employs mathematical (especially statistical) methods to analyze economic systems, to forecast economic and financial dynamics, and to develop strategies for achieving desirable economic performance. It is therefore important to develop data processing techniques that explicitly focus on prediction. The more data we have, the better our predictions will be. As such, these techniques are essential to our ability to process huge amounts of available data.
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Specificații

ISBN-13: 9783319709413
ISBN-10: 3319709410
Pagini: 480
Ilustrații: XII, 780 p. 146 illus.
Dimensiuni: 155 x 235 mm
Greutate: 1.28 kg
Ediția:1st ed. 2018
Editura: Springer International Publishing
Colecția Springer
Seria Studies in Computational Intelligence

Locul publicării:Cham, Switzerland

Cuprins

Data in the 21st Century.- The Understanding of Dependent Structure and Co-Movement of World Stock Exchanges Under the Economic Cycle.- Macro-Econometric Forecasting for During Periods of Economic Cycle Using Bayesian Extreme Value Optimization Algorithm.- Generalize Weighted in Interval Data for Fitting a Vector Autoregressive Model.- Asymmetric Effect with Quantile Regression for Interval-valued Variables.- Emissions, Trade Openness, Urbanisation, and Income in Thailand: An Empirical Analysis.- Does Forecasting Benefit from Mixed-Frequency Data Sampling Model: The Evidence from Forecasting GDP Growth Using Financial Factor in Thailand.- How Better Are Predictive Models: Analysis on the Practically Important Example of Robust Interval Uncertainty.

Textul de pe ultima copertă

This book presents recent research on predictive econometrics and big data. Gathering edited papers presented at the 11th International Conference of the Thailand Econometric Society (TES2018), held in Chiang Mai, Thailand, on January 10-12, 2018, its main focus is on predictive techniques – which directly aim at predicting economic phenomena; and big data techniques – which enable us to handle the enormous amounts of data generated by modern computers in a reasonable time. The book also discusses the applications of more traditional statistical techniques to econometric problems. Econometrics is a branch of economics that employs mathematical (especially statistical) methods to analyze economic systems, to forecast economic and financial dynamics, and to develop strategies for achieving desirable economic performance. It is therefore important to develop data processing techniques that explicitly focus on prediction. The more data we have, the better our predictions willbe. As such, these techniques are essential to our ability to process huge amounts of available data.

Caracteristici

Presents recent research on Predictive Econometrics and Big Data Introduces readers to the theoretical foundations and applications Written by respected experts in the field Includes edited papers presented at the 11th International Conference of the Thailand Econometric Society (TES2018), held in Chiang Mai, Thailand, on January 10-12, 2018