MARC

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020 |a 0-691-04289-6 
040 |a arcduce  |c arcduce 
082 0 |a 519.55 
100 |a Hamilton, James Douglas 
245 1 0 |a Time series analysis /  |c James Douglas Hamilton. 
260 |a Princeton, N.J. :  |b Princeton University Press,  |c 1994 
300 |a xiv, 799 p. :  |b il. 
504 |a Incluye bibliografía. 
505 0 |a 1. Difference equations -- 2. Lang operators -- 3. Stationary ARMA processes -- 4. Forecasting -- 5. Maximum likelihood estimation -- 6. Spectral analysis -- 7. Asymptotic distribution theory -- 8. Linear regression models -- 9. Linear systems of simultaneous equations -- 10. Covariance-stationary vector processes -- 11. Vector autoregressions -- 12. Bayesian analysis -- 13. The Kalman filter -- 14. Generalized method of moments -- 15. Models of nonstationary time series -- 16. Processes with deterministic time trends -- 17. Univariate processes with unit roots -- 18. Unit roots in multivariate time series -- 19. Cointegration -- 20. Full-information maximum likelihood analysis of cointegrated systems -- 21. Time series models of heteroscedasticity -- 22. Modeling time series with changes in regime -- A. Mathematical review -- B. Statistical tables -- C. Answers to selected exercises -- D. Greek letters and mathematical symbols used in the text. 
650 4 |a ANALISIS DE SERIES TEMPORALES  |9 438 
650 4 |a ECUACIONES DIFERENCIALES  |9 2013 
650 |a MODELO ARIMA  
650 |a TEORIA ESPECTRAL  
650 |a MODELO DE REGRESION  
650 |a ECUACIONES SIMULTANEAS  
650 |a VECTORES AUTORREGRESIVOS  
650 |a FILTRO DE KALMAN  
650 |a EJERCICIOS 
653 |a MODELO ARMAX  
653 |a ANALISIS ESPECTRAL 
856 4 |u http://www.ru.ac.bd/stat/wp-content/uploads/sites/25/2019/03/101_06_Feller_An-Introduction-to-Probability-Theory-and-Its-Applications-Vol.-2.pdf 
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952 |0 0  |1 0  |4 0  |6 519_550000000000000_H_47532  |7 0  |9 20277  |a BMB  |b BMB  |d 2010-01-01  |l 21  |o 519.55 H 47532  |p 47532  |r 2017-10-06 00:00:00  |s 2017-09-29  |u 18076  |w 2010-08-26  |y LIBR 
999 |c 16379  |d 16379