語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Seasonal Adjustment Methods and Real...
~
SpringerLink (Online service)
Seasonal Adjustment Methods and Real Time Trend-Cycle Estimation
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Seasonal Adjustment Methods and Real Time Trend-Cycle Estimation/ by Estela Bee Dagum, Silvia Bianconcini.
作者:
Bee Dagum, Estela.
其他作者:
Bianconcini, Silvia.
面頁冊數:
XVI, 283 p. 52 illus., 10 illus. in color.online resource. :
Contained By:
Springer Nature eBook
標題:
Statistics . -
電子資源:
https://doi.org/10.1007/978-3-319-31822-6
ISBN:
9783319318226
Seasonal Adjustment Methods and Real Time Trend-Cycle Estimation
Bee Dagum, Estela.
Seasonal Adjustment Methods and Real Time Trend-Cycle Estimation
[electronic resource] /by Estela Bee Dagum, Silvia Bianconcini. - 1st ed. 2016. - XVI, 283 p. 52 illus., 10 illus. in color.online resource. - Statistics for Social and Behavioral Sciences,2199-7357. - Statistics for Social and Behavioral Sciences,.
Introduction -- Time Series Components -- Part I: Seasonal Adjustment Methods -- Seasonal Adjustment: Meaning, Purpose and Methods -- Linear Filters Seasonal Adjustment Methods: Census Method II and its Variants -- Seasonal Adjustment Based on ARIMA Decomposition: TRAMO-SEATS.- Seasonal Adjustment Based on Structural Time Series Models -- Part II: Trend-Cycle Estimation.- Trend-Cycle Estimation.- Further Developments on the Henderson Trend-Cycle Filter.- A Unified View of Trend-Cycle Predictors in Reproducing Kernel Hilbert Spaces (RKHS).- Real Time Trend-Cycle Prediction.- The Effect of Seasonal Adjustment on Real-Time Trend-Cycle Prediction -- Glossary.
This book explores widely used seasonal adjustment methods and recent developments in real time trend-cycle estimation. It discusses in detail the properties and limitations of X12ARIMA, TRAMO-SEATS and STAMP - the main seasonal adjustment methods used by statistical agencies. Several real-world cases illustrate each method and real data examples can be followed throughout the text. The trend-cycle estimation is presented using nonparametric techniques based on moving averages, linear filters and reproducing kernel Hilbert spaces, taking recent advances into account. The book provides a systematical treatment of results that to date have been scattered throughout the literature. Seasonal adjustment and real time trend-cycle prediction play an essential part at all levels of activity in modern economies. They are used by governments to counteract cyclical recessions, by central banks to control inflation, by decision makers for better modeling and planning and by hospitals, manufacturers, builders, transportation, and consumers in general to decide on appropriate action. This book appeals to practitioners in government institutions, finance and business, macroeconomists, and other professionals who use economic data as well as academic researchers in time series analysis, seasonal adjustment methods, filtering and signal extraction. It is also useful for graduate and final-year undergraduate courses in econometrics and time series with a good understanding of linear regression and matrix algebra, as well as ARIMA modelling.
ISBN: 9783319318226
Standard No.: 10.1007/978-3-319-31822-6doiSubjects--Topical Terms:
1253516
Statistics .
LC Class. No.: QA276-280
Dewey Class. No.: 330.015195
Seasonal Adjustment Methods and Real Time Trend-Cycle Estimation
LDR
:03720nam a22004215i 4500
001
977865
003
DE-He213
005
20200629211848.0
007
cr nn 008mamaa
008
201211s2016 gw | s |||| 0|eng d
020
$a
9783319318226
$9
978-3-319-31822-6
024
7
$a
10.1007/978-3-319-31822-6
$2
doi
035
$a
978-3-319-31822-6
050
4
$a
QA276-280
072
7
$a
PBT
$2
bicssc
072
7
$a
BUS061000
$2
bisacsh
072
7
$a
PBT
$2
thema
072
7
$a
K
$2
thema
082
0 4
$a
330.015195
$2
23
100
1
$a
Bee Dagum, Estela.
$e
author.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1271476
245
1 0
$a
Seasonal Adjustment Methods and Real Time Trend-Cycle Estimation
$h
[electronic resource] /
$c
by Estela Bee Dagum, Silvia Bianconcini.
250
$a
1st ed. 2016.
264
1
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2016.
300
$a
XVI, 283 p. 52 illus., 10 illus. in color.
$b
online resource.
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
347
$a
text file
$b
PDF
$2
rda
490
1
$a
Statistics for Social and Behavioral Sciences,
$x
2199-7357
505
0
$a
Introduction -- Time Series Components -- Part I: Seasonal Adjustment Methods -- Seasonal Adjustment: Meaning, Purpose and Methods -- Linear Filters Seasonal Adjustment Methods: Census Method II and its Variants -- Seasonal Adjustment Based on ARIMA Decomposition: TRAMO-SEATS.- Seasonal Adjustment Based on Structural Time Series Models -- Part II: Trend-Cycle Estimation.- Trend-Cycle Estimation.- Further Developments on the Henderson Trend-Cycle Filter.- A Unified View of Trend-Cycle Predictors in Reproducing Kernel Hilbert Spaces (RKHS).- Real Time Trend-Cycle Prediction.- The Effect of Seasonal Adjustment on Real-Time Trend-Cycle Prediction -- Glossary.
520
$a
This book explores widely used seasonal adjustment methods and recent developments in real time trend-cycle estimation. It discusses in detail the properties and limitations of X12ARIMA, TRAMO-SEATS and STAMP - the main seasonal adjustment methods used by statistical agencies. Several real-world cases illustrate each method and real data examples can be followed throughout the text. The trend-cycle estimation is presented using nonparametric techniques based on moving averages, linear filters and reproducing kernel Hilbert spaces, taking recent advances into account. The book provides a systematical treatment of results that to date have been scattered throughout the literature. Seasonal adjustment and real time trend-cycle prediction play an essential part at all levels of activity in modern economies. They are used by governments to counteract cyclical recessions, by central banks to control inflation, by decision makers for better modeling and planning and by hospitals, manufacturers, builders, transportation, and consumers in general to decide on appropriate action. This book appeals to practitioners in government institutions, finance and business, macroeconomists, and other professionals who use economic data as well as academic researchers in time series analysis, seasonal adjustment methods, filtering and signal extraction. It is also useful for graduate and final-year undergraduate courses in econometrics and time series with a good understanding of linear regression and matrix algebra, as well as ARIMA modelling.
650
0
$a
Statistics .
$3
1253516
650
0
$a
Macroeconomics.
$3
554837
650
0
$a
Probabilities.
$3
527847
650
0
$a
Econometrics.
$3
556981
650
1 4
$a
Statistics for Business, Management, Economics, Finance, Insurance.
$3
1211158
650
2 4
$a
Statistical Theory and Methods.
$3
671396
650
2 4
$a
Statistics for Social Sciences, Humanities, Law.
$3
1211304
650
2 4
$a
Macroeconomics/Monetary Economics//Financial Economics.
$3
1069052
650
2 4
$a
Probability Theory and Stochastic Processes.
$3
593945
700
1
$a
Bianconcini, Silvia.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1110649
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9783319318202
776
0 8
$i
Printed edition:
$z
9783319318219
776
0 8
$i
Printed edition:
$z
9783319811277
830
0
$a
Statistics for Social and Behavioral Sciences,
$x
2199-7357
$3
1254189
856
4 0
$u
https://doi.org/10.1007/978-3-319-31822-6
912
$a
ZDB-2-SMA
912
$a
ZDB-2-SXMS
950
$a
Mathematics and Statistics (SpringerNature-11649)
950
$a
Mathematics and Statistics (R0) (SpringerNature-43713)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入