大葉大學圖書館 |

Time series econometrics[electronic resource] /

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	330.015
書名/作者:	Time series econometrics/ by Klaus Neusser.
作者:	Neusser, Klaus.
出版者:	Cham : : Springer International Publishing :, 2016.
面頁冊數:	xxiv, 409 p. : : ill., digital ;; 26 cm.
Contained By:	Springer eBooks
標題:	Econometric models.
標題:	Economics.
標題:	Econometrics.
標題:	Macroeconomics/Monetary Economics/Financial Economics.
標題:	Statistics for Business/Economics/Mathematical Finance/Insurance.
ISBN:	9783319328621
ISBN:	9783319328614
內容註:	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.
摘要、提要註:	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.
電子資源:	http://dx.doi.org/10.1007/978-3-319-32862-1

Time series econometrics[electronic resource] /
Neusser, Klaus.

Time series econometrics[electronic resource] /by Klaus Neusser. - Cham :Springer International Publishing :2016. - xxiv, 409 p. :ill., digital ;26 cm. - 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:

184596
Econometric models.

LC Class. No.: HB141 / .N48 2016

Dewey Class. No.: 330.015

Time series econometrics[electronic resource] /
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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.
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