000 04947nam a22005655i 4500
001 978-981-10-6436-4
003 DE-He213
005 20181204134423.0
007 cr nn 008mamaa
008 171027s2017 si | s |||| 0|eng d
020 _a9789811064364
_9978-981-10-6436-4
024 7 _a10.1007/978-981-10-6436-4
_2doi
040 _aISI Library, Kolkata
050 4 _aQA276-280
072 7 _aPBT
_2bicssc
072 7 _aMAT029000
_2bisacsh
072 7 _aPBT
_2thema
082 0 4 _a519.5
_223
100 1 _aHosoya, Yuzo.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aCharacterizing Interdependencies of Multiple Time Series
_h[electronic resource] :
_bTheory and Applications /
_cby Yuzo Hosoya, Kosuke Oya, Taro Takimoto, Ryo Kinoshita.
264 1 _aSingapore :
_bSpringer Singapore :
_bImprint: Springer,
_c2017.
300 _aX, 133 p. 32 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aJSS Research Series in Statistics,
_x2364-0057
505 0 _a1: Introduction to statistical causal analysis -- 2: Measures of one-way effect, reciprocity and association -- 3: Partial measures of interdependence -- 4: Inference based on the vector autoregressive and moving average model -- 5: Inference on change in causality measures -- 6: Simulation performance of estimation methods -- 7: Empirical analysis of macroeconomic series -- 8: Empirical analysis of change in causality measures -- 9: Conclusion -- Appendix -- References -- Index.
520 _aThis book introduces academic researchers and professionals to the basic concepts and methods for characterizing interdependencies of multiple time series in the frequency domain. Detecting causal directions between a pair of time series and the extent of their effects, as well as testing the non existence of a feedback relation between them, have constituted major focal points in multiple time series analysis since Granger introduced the celebrated definition of causality in view of prediction improvement. Causality analysis has since been widely applied in many disciplines. Although most analyses are conducted from the perspective of the time domain, a frequency domain method introduced in this book sheds new light on another aspect that disentangles the interdependencies between multiple time series in terms of long-term or short-term effects, quantitatively characterizing them. The frequency domain method includes the Granger noncausality test as a special case. Chapters 2 and 3 of the book introduce an improved version of the basic concepts for measuring the one-way effect, reciprocity, and association of multiple time series, which were originally proposed by Hosoya. Then the statistical inferences of these measures are presented, with a focus on the stationary multivariate autoregressive moving-average processes, which include the estimation and test of causality change. Empirical analyses are provided to illustrate what alternative aspects are detected and how the methods introduced here can be conveniently applied. Most of the materials in Chapters 4 and 5 are based on the authors' latest research work. Subsidiary items are collected in the Appendix.
650 0 _aMathematical statistics.
650 0 _aStatistics.
650 1 4 _aStatistical Theory and Methods.
_0http://scigraph.springernature.com/things/product-market-codes/S11001
650 2 4 _aStatistics for Life Sciences, Medicine, Health Sciences.
_0http://scigraph.springernature.com/things/product-market-codes/S17030
650 2 4 _aStatistics for Business/Economics/Mathematical Finance/Insurance.
_0http://scigraph.springernature.com/things/product-market-codes/S17010
650 2 4 _aStatistics for Social Science, Behavorial Science, Education, Public Policy, and Law.
_0http://scigraph.springernature.com/things/product-market-codes/S17040
650 2 4 _aStatistics and Computing/Statistics Programs.
_0http://scigraph.springernature.com/things/product-market-codes/S12008
650 2 4 _aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
_0http://scigraph.springernature.com/things/product-market-codes/S17020
700 1 _aOya, Kosuke.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aTakimoto, Taro.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aKinoshita, Ryo.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9789811064357
776 0 8 _iPrinted edition:
_z9789811064371
830 0 _aJSS Research Series in Statistics,
_x2364-0057
856 4 0 _uhttps://doi.org/10.1007/978-981-10-6436-4
912 _aZDB-2-SMA
942 _cEB
950 _aMathematics and Statistics (Springer-11649)
999 _c427346
_d427346