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Predictive econometrics and big data

Kreinovich, Vladik.

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Predictive econometrics and big data

Record Type:	Electronic resources : Monograph/item
Title/Author:	Predictive econometrics and big data/ edited by Vladik Kreinovich, Songsak Sriboonchitta, Nopasit Chakpitak.
other author:	Kreinovich, Vladik.
Published:	Cham :Springer International Publishing : : 2018.,
Description:	xii, 780 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Econometrics. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-70942-0
ISBN:	9783319709420

Predictive econometrics and big data
Predictive econometrics and big data[electronic resource] /edited by Vladik Kreinovich, Songsak Sriboonchitta, Nopasit Chakpitak. - Cham :Springer International Publishing :2018. - xii, 780 p. :ill., digital ;24 cm. - Studies in computational intelligence,v.7531860-949X ;. - Studies in computational intelligence ;v.753..

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.

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.

ISBN: 9783319709420

Standard No.: 10.1007/978-3-319-70942-0doiSubjects--Topical Terms:

542934
Econometrics.

LC Class. No.: HB139

Dewey Class. No.: 330.015195

Predictive econometrics and big data
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245 0 0 $a Predictive econometrics and big data $h [electronic resource] / $c edited by Vladik Kreinovich, Songsak Sriboonchitta, Nopasit Chakpitak.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a xii, 780 p. : $b ill., digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-949X ; $v v.753
505 0 $a 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.
520 $a 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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650 1 4 $a Engineering. $3 586835
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 890894
700 1 $a Kreinovich, Vladik. $3 1965595
700 1 $a Sriboonchitta, Songsak. $3 2056845
700 1 $a Chakpitak, Nopasit. $3 3299742
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773 0 $t Springer eBooks
830 0 $a Studies in computational intelligence ; $v v.753. $3 3299743
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-70942-0
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