000			05657nam a22002417a 4500
999			_c56485 _d56485
005			20200312102305.0
008			200312b ||||| |||| 00| 0 eng d
020			_a9783319328614
028			_aInternational Book Distributors _bFlat No.17, Prakash Apartments,5 Ansari Road, New Delhi-110002 _c115245 _d25/02/2020
082			_a330.0151 NEU
100			_aKlaus Neusser
245			_aTime Series Econometrics _c/Klaus Neusser
260			_aSwitzerland _b: Springer _c, 2016
300			_axxiv, 409 pages _c;24 cm.
365			_b7297
490			_aSpringer texts in business and economics.
505			_aPART I. UNIVARIATE TIME SERIES ANALYSIS -- 1. Introduction ; 1.1. Some Examples ; 1.2. Formal Definitions ; 1.3. Stationarity ; 1.4. Construction of Stochastic Processes ; 1.5. Properties of the Autocovariance Function ; 1.6. Exercises -- 2. ARMA Models ; 2.1. The Lag Operator ; 2.2. Some Important Special Cases ; 2.3. Causality and Invertibility ; 2.4. Computation of Autocovariance Function ; 2.5. Exercises -- 3. Forecasting Stationary Processes ; 3.1. Linear Least-Squares Forecasts ; 3.2. The Wold Decomposition Theorem ; 3.3. Exponential Smoothing ; 3.4. Exercises ; 3.5. Partial Autocorrelation ; 3.6. Exercises -- 4. Estimation of Mean and ACF ; 4.1. Estimation of the Mean ; 4.2. Estimation of ACF ; 4.3. Estimation of PACF ; 4.4. Estimation of the Long-Run Variance ; 4.5. Exercises -- 5. Estimation of ARMA Models ; 5.1. The Yule-Walker Estimator ; 5.2. OLS Estimation of an AR(p) Model ; 5.3. Estimation of an ARMA(p, q) Model ; 5.4. Estimation of the Orders p and q ; 5.5. Modeling a Stochastic Process ; 5.6. Modeling Real GDP of Switzerland -- 6. Spectral Analysis and Linear Filters ; 6.1. Spectral Density ; 6.2. Spectral Decomposition of a Time Series ; 6.3. The Periodogram and the Estimation of Spectral Densities ; 6.4. Linear Time-Invariant Filters ; 6.5. Some Important Filters ; 6.6. Exercises -- 7. Integrated Processes ; 7.1. Definition, Properties and Interpretation ; 7.2. Properties of the OLS Estimator in the Case of Integrated Variables ; 7.3. Unit-Root Tests ; 7.4. Generalizations of Unit-Root Tests ; 7.5. Regression with Integrated Variables -- 8. Models of Volatility ; 8.1. Specification and Interpretation ; 8.2. Tests for Heteroskedasticity ; 8.3. Estimation of GARCH(p, q) Models ; 8.4. Example: Swiss Market Index (SMI). PART II. MULTIVARIATE TIME SERIES ANALYSIS -- 9. Introduction -- 10. Definitions and Stationarity -- 11. Estimation of Covariance Function ; 11.1. Estimators and Asymptotic Distributions ; 11.2. Testing Cross-Correlations of Time Series ; 11.3. Some Examples for Independence Tests -- 12. VARMA Processes ; 12.1. The VAR(1) Process ; 12.2. Representation in Companion Form ; 12.3. Causal Representation ; 12.4. Computation of Covariance Function -- 13. Estimation of VAR Models ; 13.1. Introduction ; 13.2. The Least-Squares Estimator ; 13.3. Proofs of Asymptotic Normality ; 13.4. The Yule-Walker Estimator -- 14. Forecasting with VAR Models ; 14.1. Forecasting with Known Parameters ; 14.2. Forecasting with Estimated Parameters ; 14.3. Modeling of VAR Models ; 14.4. Example: VAR Model -- 15. Interpretation of VAR Models ; 15.1. Wiener-Granger Causality ; 15.2. Structural and Reduced Form ; 15.3. Identification via Short-Run Restrictions ; 15.4. Interpretation of VAR Models ; 15.5. Identification via Long-Run Restrictions ; 15.6. Sign Restrictions -- 16. Cointegration ; 16.1. A Theoretical Example ; 16.2. Definition and Representation ; 16.3. Johansen's Cointegration Test ; 16.4. Estimation and Testing of Cointegrating Relationships ; 16.5. An Example -- 17. Kalman Filter ; 17.1. The State Space Model ; 17.2. Filtering and Smoothing ; 17.3. Estimation of State Space Models ; 17.4. Examples ; 17.5. Exercises -- 18. Generalizations of Linear Models ; 18.1. Structural Breaks ; 18.2. Time-Varying Parameters ; 18.3. Regime Switching Models -- A. Complex Numbers -- B. Linear Difference Equations -- C. Stochastic Convergence -- D. BN-Decomposition -- E. The Delta Method.
520			_aThis 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			_aEconomics
655			_aTime-series analysis _bEconometric models
700			_aKlaus Neusser
942			_cBOOK