國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Seasonal adjustment methods and real...

Bianconcini, Silvia.

Seasonal adjustment methods and real time trend-cycle estimation

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Seasonal adjustment methods and real time trend-cycle estimationby Estela Bee Dagum, Silvia Bianconcini.
作者:	Dagum, Estela Bee.
其他作者:	Bianconcini, Silvia.
出版者:	Cham :Springer International Publishing :2016.
面頁冊數:	xvi, 283 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
標題:	Seasonal variations (Economics)
電子資源:	http://dx.doi.org/10.1007/978-3-319-31822-6
ISBN:	9783319318226$q(electronic bk.)

Seasonal adjustment methods and real time trend-cycle estimation
Dagum, Estela Bee.

Seasonal adjustment methods and real time trend-cycle estimation[electronic resource] /by Estela Bee Dagum, Silvia Bianconcini. - Cham :Springer International Publishing :2016. - xvi, 283 p. :ill. (some col.), digital ;24 cm. - 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$q(electronic bk.)

Standard No.: 10.1007/978-3-319-31822-6doiSubjects--Topical Terms:

647619
Seasonal variations (Economics)

LC Class. No.: HB137

Dewey Class. No.: 338.54

Seasonal adjustment methods and real time trend-cycle estimation
LDR:03292nmm a2200337 a 4500 001 490734
003 DE-He213
005 20161130113204.0
006 m d
007 cr nn 008maaau
008 170118s2016 gw s 0 eng d
020 $a 9783319318226$q(electronic bk.)
020 $a 9783319318202$q(paper)
024 7 $a 10.1007/978-3-319-31822-6 $2 doi
035 $a 978-3-319-31822-6
040 $a GP $c GP
041 0 $a eng
050 4 $a HB137
072 7 $a PBT $2 bicssc
072 7 $a K $2 bicssc
072 7 $a BUS061000 $2 bisacsh
082 0 4 $a 338.54 $2 23
090 $a HB137 $b .D128 2016
100 1 $a Dagum, Estela Bee. $3 261339
245 1 0 $a Seasonal adjustment methods and real time trend-cycle estimation $h [electronic resource] / $c by Estela Bee Dagum, Silvia Bianconcini.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a xvi, 283 p. : $b ill. (some col.), digital ; $c 24 cm.
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 Seasonal variations (Economics) $3 647619
650 0 $a Estimation theory. $3 181864
650 1 4 $a Statistics. $3 182057
650 2 4 $a Statistics for Business/Economics/Mathematical Finance/Insurance. $3 274062
650 2 4 $a Statistical Theory and Methods. $3 274054
650 2 4 $a Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law. $3 274394
650 2 4 $a Macroeconomics/Monetary Economics/Financial Economics. $3 737439
650 2 4 $a Probability Theory and Stochastic Processes. $3 274061
650 2 4 $a Econometrics. $3 182271
700 1 $a Bianconcini, Silvia. $3 750307
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Statistics for social and behavioral sciences. $3 560039
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-31822-6
950 $a Mathematics and Statistics (Springer-11649)