000 | 04021nam a22005655i 4500 | ||
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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 |
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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 |
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_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. |
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300 |
_aXII, 780 p. 146 illus. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aStudies in Computational Intelligence, _x1860-9503 ; _v753 |
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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 |
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650 | 0 |
_aArtificial intelligence. _93407 |
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650 | 0 |
_aEconometrics. _920971 |
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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 |
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700 | 1 |
_aSriboonchitta, Songsak. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _953292 |
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700 | 1 |
_aChakpitak, Nopasit. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _953293 |
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710 | 2 |
_aSpringerLink (Online service) _953294 |
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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 |