Predictive econometrics and big data
- Responsibility
- Vladik Kreinovich, Songsak Sriboonchitta, Nopasit Chakpitak, editors.
- Digital
- text file
- Publication
- Cham, Switzerland : Springer, 2018.
- Physical description
- 1 online resource (xii, 780 pages) : illustrations
- Series
- Studies in computational intelligence ; 753. 1860-949X
Online
Available online
More options
Description
Creators/Contributors
- Contributor
- Kreinovich, Vladik editor.
- Songsak Sriboonchitta, editor.
- Chakpitak, Nopasit, editor.
Contents/Summary
- Contents
-
- 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.
- (source: Nielsen Book Data)
- Publisher's summary
-
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.
(source: Nielsen Book Data)
Subjects
Bibliographic information
- Publication date
- 2018
- Series
- Studies in computational intelligence, 1860-949X ; volume 753
- Note
- Includes author index.
- ISBN
- 9783319709420 (electronic bk.)
- 3319709429 (electronic bk.)
- 9783319709413 (print)
- 3319709410
- DOI
- 10.1007/978-3-319-70942-0
