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001 978-3-319-70942-0
003 DE-He213
005 20220801220940.0
007 cr nn 008mamaa
008 171201s2018 sz | s |||| 0|eng d
020 _a9783319709420
_9978-3-319-70942-0
024 7 _a10.1007/978-3-319-70942-0
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aTEC009000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
245 1 0 _aPredictive Econometrics and Big Data
_h[electronic resource] /
_cedited by Vladik Kreinovich, Songsak Sriboonchitta, Nopasit Chakpitak.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aXII, 780 p. 146 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v753
505 0 _aData 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 _aThis 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.
650 0 _aComputational intelligence.
_97716
650 0 _aArtificial intelligence.
_93407
650 0 _aEconometrics.
_920971
650 1 4 _aComputational Intelligence.
_97716
650 2 4 _aArtificial Intelligence.
_93407
650 2 4 _aEconometrics.
_920971
700 1 _aKreinovich, Vladik.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_953291
700 1 _aSriboonchitta, Songsak.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_953292
700 1 _aChakpitak, Nopasit.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_953293
710 2 _aSpringerLink (Online service)
_953294
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319709413
776 0 8 _iPrinted edition:
_z9783319709437
776 0 8 _iPrinted edition:
_z9783319890180
830 0 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v753
_953295
856 4 0 _uhttps://doi.org/10.1007/978-3-319-70942-0
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c79121
_d79121