國立虎尾科技大學 |

Time Series Econometrics

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Time Series Econometrics/ by Klaus Neusser.
作者:	Neusser, Klaus.
面頁冊數:	XXIV, 409 p. 66 illus., 64 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Econometrics. -
電子資源:	https://doi.org/10.1007/978-3-319-32862-1
ISBN:	9783319328621

Time Series Econometrics
Neusser, Klaus.

Time Series Econometrics[electronic resource] /by Klaus Neusser. - 1st ed. 2016. - XXIV, 409 p. 66 illus., 64 illus. in color.online resource. - Springer Texts in Business and Economics,2192-4333. - Springer Texts in Business and Economics,.

1. Introduction -- 2. ARMA models -- 3. Forecasting stationary processes -- 4. Estimation of Mean and Autocovariance Function -- 5.Estimation of ARMA Models -- 6. Spectral Analysis and Linear Filters -- 7. Integrated Processes -- 8. Models of Volatility -- 9. Multivariate Time series -- 10. Estimation of Covariance Function -- 11. VARMA Processes -- 12. Estimation of VAR Models -- 13. Forecasting with VAR Models -- 14. Interpretation of VAR Models -- 15. Co-integration -- 16. The Kalman Filter -- 17. Appendices.

This text presents modern developments in time series analysis and focuses on their application to economic problems. The book first introduces the fundamental concept of a stationary time series and the basic properties of covariance, investigating the structure and estimation of autoregressive-moving average (ARMA) models and their relations to the covariance structure. The book then moves on to non-stationary time series, highlighting its consequences for modeling and forecasting and presenting standard statistical tests and regressions. Next, the text discusses volatility models and their applications in the analysis of financial market data, focusing on generalized autoregressive conditional heteroskedastic (GARCH) models. The second part of the text devoted to multivariate processes, such as vector autoregressive (VAR) models and structural vector autoregressive (SVAR) models, which have become the main tools in empirical macroeconomics. The text concludes with a discussion of co-integrated models and the Kalman Filter, which is being used with increasing frequency. Mathematically rigorous, yet application-oriented, this self-contained text will help students develop a deeper understanding of theory and better command of the models that are vital to the field. Assuming a basic knowledge of statistics and/or econometrics, this text is best suited for advanced undergraduate and beginning graduate students. .

ISBN: 9783319328621

Standard No.: 10.1007/978-3-319-32862-1doiSubjects--Topical Terms:

556981
Econometrics.

LC Class. No.: HB139-141

Dewey Class. No.: 330.015195

Time Series Econometrics
LDR:03348nam a22004095i 4500 001 971731
003 DE-He213
005 20200630231520.0
007 cr nn 008mamaa
008 201211s2016 gw | s |||| 0|eng d
020 $a 9783319328621 $9 978-3-319-32862-1
024 7 $a 10.1007/978-3-319-32862-1 $2 doi
035 $a 978-3-319-32862-1
050 4 $a HB139-141
072 7 $a KCH $2 bicssc
072 7 $a BUS021000 $2 bisacsh
072 7 $a KCH $2 thema
082 0 4 $a 330.015195 $2 23
100 1 $a Neusser, Klaus. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1110203
245 1 0 $a Time Series Econometrics $h [electronic resource] / $c by Klaus Neusser.
250 $a 1st ed. 2016.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a XXIV, 409 p. 66 illus., 64 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 Springer Texts in Business and Economics, $x 2192-4333
505 0 $a 1. Introduction -- 2. ARMA models -- 3. Forecasting stationary processes -- 4. Estimation of Mean and Autocovariance Function -- 5.Estimation of ARMA Models -- 6. Spectral Analysis and Linear Filters -- 7. Integrated Processes -- 8. Models of Volatility -- 9. Multivariate Time series -- 10. Estimation of Covariance Function -- 11. VARMA Processes -- 12. Estimation of VAR Models -- 13. Forecasting with VAR Models -- 14. Interpretation of VAR Models -- 15. Co-integration -- 16. The Kalman Filter -- 17. Appendices.
520 $a This text presents modern developments in time series analysis and focuses on their application to economic problems. The book first introduces the fundamental concept of a stationary time series and the basic properties of covariance, investigating the structure and estimation of autoregressive-moving average (ARMA) models and their relations to the covariance structure. The book then moves on to non-stationary time series, highlighting its consequences for modeling and forecasting and presenting standard statistical tests and regressions. Next, the text discusses volatility models and their applications in the analysis of financial market data, focusing on generalized autoregressive conditional heteroskedastic (GARCH) models. The second part of the text devoted to multivariate processes, such as vector autoregressive (VAR) models and structural vector autoregressive (SVAR) models, which have become the main tools in empirical macroeconomics. The text concludes with a discussion of co-integrated models and the Kalman Filter, which is being used with increasing frequency. Mathematically rigorous, yet application-oriented, this self-contained text will help students develop a deeper understanding of theory and better command of the models that are vital to the field. Assuming a basic knowledge of statistics and/or econometrics, this text is best suited for advanced undergraduate and beginning graduate students. .
650 0 $a Econometrics. $3 556981
650 0 $a Macroeconomics. $3 554837
650 0 $a Statistics . $3 1253516
650 2 4 $a Macroeconomics/Monetary Economics//Financial Economics. $3 1069052
650 2 4 $a Statistics for Business, Management, Economics, Finance, Insurance. $3 1211158
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319328614
776 0 8 $i Printed edition: $z 9783319328638
776 0 8 $i Printed edition: $z 9783319813875
830 0 $a Springer Texts in Business and Economics, $x 2192-4333 $3 1253711
856 4 0 $u https://doi.org/10.1007/978-3-319-32862-1
912 $a ZDB-2-ECF
912 $a ZDB-2-SXEF
950 $a Economics and Finance (SpringerNature-41170)
950 $a Economics and Finance (R0) (SpringerNature-43720)