New introduction to multiple time series analysis /

New introduction to multiple time series analysis /

Saved in:
Bibliographic Details
Author / Creator:Lütkepohl, Helmut.
Imprint:Berlin : New York : Springer, 2005.
Description:1 online resource (xxi, 764 p.) : ill.
Language:English
Subject:
Time-series analysis.
Time-series analysis.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/9009676
Hidden Bibliographic Details
ISBN:9783540277521 (electronic bk.)
3540277528 (electronic bk.)
3540401725
9783540401728
Notes:Includes bibliographical references (p. 713-732) and indexes.
Description based on print version record.
Summary:Deals with analyzing and forecasting multiple time series, considering a range of models and methods. This reference work and graduate-level textbook enables readers to perform their analyses in a competent manner.
Other form:Print version: Lütkepohl, Helmut. New introduction to multiple time series analysis. Berlin : New York : Springer, 2005 3540401725 9783540401728

MARC

LEADER 00000cam a2200000Ia 4500
001 9009676
003 ICU
005 20140228112500.0
006 m o d
007 cr cn|
008 081017s2005 gw a ob 001 0 eng d
019 |a 62762502  |a 228377152  |a 320977813  |a 647590816  |a 685362506  |a 698451295  |a 756421106 
020 |a 9783540277521 (electronic bk.) 
020 |a 3540277528 (electronic bk.) 
020 |z 3540401725 
020 |z 9783540401728 
035 |a (OCoLC)262681472  |z (OCoLC)62762502  |z (OCoLC)228377152  |z (OCoLC)320977813  |z (OCoLC)647590816  |z (OCoLC)685362506  |z (OCoLC)698451295  |z (OCoLC)756421106 
037 |a 978-3-540-40172-8  |b Springer  |n http://www.springerlink.com 
040 |a GW5XE  |b eng  |c GW5XE  |d OCLCQ  |d MHW  |d N$T  |d YDXCP  |d TUU  |d UAB  |d CNTRU  |d E7B  |d IDEBK  |d YNG  |d OCLCQ  |d GW5XE  |d A7U  |d ZMC 
049 |a CGUA 
072 7 |a QA  |2 lcco 
072 7 |a HB  |2 lcco 
072 7 |a MAT  |x 029050  |2 bisacsh 
082 0 4 |a 519.5/5  |2 22 
090 |a QA280  |b .L87 2005eb 
100 1 |a Lütkepohl, Helmut.  |0 http://id.loc.gov/authorities/names/n86090773  |1 http://viaf.org/viaf/85276947 
245 1 0 |a New introduction to multiple time series analysis /  |c Helmut Lütkepohl. 
260 |a Berlin :  |b New York :  |b Springer,  |c 2005. 
300 |a 1 online resource (xxi, 764 p.) :  |b ill. 
336 |a text  |b txt  |2 rdacontent  |0 http://id.loc.gov/vocabulary/contentTypes/txt 
337 |a computer  |b c  |2 rdamedia  |0 http://id.loc.gov/vocabulary/mediaTypes/c 
338 |a online resource  |b cr  |2 rdacarrier  |0 http://id.loc.gov/vocabulary/carriers/cr 
504 |a Includes bibliographical references (p. 713-732) and indexes. 
505 0 0 |g 1.  |t Objectives of Analyzing Multiple Time Series --  |t Some Basics --  |t Vector Autoregressive Processes --  |t Outline of the Following Chapters --  |g Part I.  |t Finite Order Vector Autoregressive Processes --  |t 2.  |t Stable Vector Autoregressive Processes --  |t Basic Assumptions and Properties of VAR Processes --  |t Stable VAR(p) Processes --  |t The Moving Average Representation of a VAR Process --  |t Stationary Processes --  |t Computation of Autocovariances and Autocorrelations of Stable VAR Processes --  |t Forecasting --  |t The Loss Function --  |t Point Forecasts --  |t Interval Forecasts and Forecast Regions --  |t Structural Analysis with VAR Models --  |t Granger-Causality, Instantaneous Causality, and Multi-Step Causality --  |t Impulse Response Analysis --  |t Forecast Error Variance Decomposition --  |t Remarks on the Interpretation of VAR Models --  |g 3.  |t Estimation of Vector Autoregressive Processes --  |t Multivariate Least Squares Estimation --  |t The Estimator --  |t Asymptotic Properties of the Least Squares Estimator -- 
505 8 0 |t Small Sample Properties of the LS Estimator --  |t Least Squares Estimation with Mean-Adjusted Data and Yule-Walker Estimation --  |t Estimation when the Process Mean Is Known --  |t Estimation of the Process Mean --  |t Estimation with Unknown Process Mean --  |t The Yule-Walker Estimator --  |t Maximum Likelihood Estimation --  |t The Likelihood Function --  |t The ML Estimators --  |t Properties of the ML Estimators --  |t Forecasting with Estimated Processes --  |t General Assumptions and Results --  |t The Approximate MSE Matrix --  |t A Small Sample Investigation --  |t Testing for Causality --  |t A Wald Test for Granger-Causality --  |t Testing for Instantaneous Causality --  |t Testing for Multi-Step Causality --  |t The Asymptotic Distributions of Impulse Responses and Forecast Error Variance Decompositions --  |t The Main Results --  |t Proof of Proposition 3.6 --  |t Investigating the Distributions of the Impulse Responses by Simulation Techniques --  |t Algebraic Problems --  |t Numerical Problems --  |g 4 .  |t VAR Order Selection and Checking the Model Adequacy -- 
505 8 0 |t A Sequence of Tests for Determining the VAR Order --  |t The Impact of the Fitted VAR Order on the Forecast MSE --  |t The Likelihood Ratio Test Statistic --  |t A Testing Scheme for VAR Order Determination --  |t Criteria for VAR Order Selection --  |t Minimizing the Forecast MSE --  |t Consistent Order Selection --  |t Comparison of Order Selection Criteria --  |t Some Small Sample Simulation Results --  |t Checking the Whiteness of the Residuals --  |t The Asymptotic Distributions of the Autocovariances and Autocorrelations of a White Noise Process --  |t The Asymptotic Distributions of the Residual Autocovariances and Autocorrelations of an Estimated VAR Process --  |t Portmanteau Tests --  |t Lagrange Multiplier Tests --  |t Testing for Nonnormality --  |t Tests for Nonnormality of a Vector White Noise Process --  |t Tests for Nonnormality of a VAR Process --  |t Tests for Structural Change --  |t Chow Tests --  |t Forecast Tests for Structural Change --  |t Algebraic Problems --  |t Numerical Problems --  |g 5.  |t VAR Processes with Parameter Constraints -- 
505 8 0 |t Linear Constraints --  |t The Model and the Constraints --  |t LS, GLS, and EGLS Estimation --  |t Maximum Likelihood Estimation --  |t Constraints for Individual Equations --  |t Restrictions for the White Noise Covariance Matrix --  |t Forecasting --  |t Impulse Response Analysis and Forecast Error Variance Decomposition --  |t Specification of Subset VAR Models --  |t Model Checking --  |t VAR Processes with Nonlinear Parameter Restrictions --  |t Bayesian Estimation --  |t Basic Terms and Notation --  |t Normal Priors for the Parameters of a Gaussian VAR Process --  |t The Minnesota or Litterman Prior --  |t Practical Considerations --  |t Classical versus Bayesian Interpretation of [̄alpha] in Forecasting and Structural Analysis --  |t Algebraic Exercises --  |t Numerical Problems --  |g Part II.  |t Cointegrated Processes --  |g 6.  |t Vector Error Correction Models --  |t Integrated Processes --  |t VAR Processes with Integrated Variables --  |t Cointegrated Processes, Common Stochastic Trends, and Vector Error Correction Models --  |t Deterministic Terms in Cointegrated Processes -- 
505 8 0 |t Forecasting Integrated and Cointegrated Variables --  |t Causality Analysis --  |t Impulse Response Analysis --  |g 7 .  |t Estimation of Vector Error Correction Models --  |t Estimation of a Simple Special Case VECM --  |t Estimation of General VECMs --  |t LS Estimation --  |t EGLS Estimation of the Cointegration Parameters --  |t ML Estimation --  |t Including Deterministic Terms --  |t Other Estimation Methods for Cointegrated Systems --  |t Estimating VECMs with Parameter Restrictions --  |t Linear Restrictions for the Cointegration Matrix --  |t Linear Restrictions for the Short-Run and Loading Parameters --  |t Bayesian Estimation of Integrated Systems --  |t The Model Setup --  |t The Minnesota or Litterman Prior --  |t Forecasting Estimated Integrated and Cointegrated Systems --  |t Testing for Granger-Causality --  |t The Noncausality Restrictions --  |t Problems Related to Standard Wald Tests --  |t A Wald Test Based on a Lag Augmented VAR --  |t Impulse Response Analysis --  |t Algebraic Exercises --  |t Numerical Exercises --  |g 8.  |t Specification of VECMs --  |t Lag Order Selection -- 
505 8 0 |t Testing for the Rank of Cointegration --  |t A VECM without Deterministic Terms --  |t A Nonzero Mean Term --  |t A Linear Trend --  |t A Linear Trend in the Variables and Not in the Cointegration Relations --  |t Summary of Results and Other Deterministic Terms --  |t Prior Adjustment for Deterministic Terms --  |t Choice of Deterministic Terms --  |t Other Approaches to Testing for the Cointegrating Rank342 --  |t Subset VECMs --  |t Model Diagnostics --  |t Checking for Residual Autocorrelation --  |t Testing for Nonnormality --  |t Tests for Structural Change --  |t Algebraic Exercises --  |t Numerical Exercises --  |g Part III.  |t Structural and Conditional Models --  |g 9.  |t Structural VARs and VECMs --  |t Structural Vector Autoregressions --  |t The A-Model --  |t The B-Model --  |t The AB-Model --  |t Long-Run Restrictions `a la Blanchard-Quah --  |t Structural Vector Error Correction Models --  |t Estimation of Structural Parameters --  |t Estimating SVAR Models --  |t Estimating Structural VECMs --  |t Impulse Response Analysis and Forecast Error Variance Decomposition --  |t Further Issues -- 
505 8 0 |t Algebraic Problems --  |t Numerical Problems --  |g 10.  |t Systems of Dynamic Simultaneous Equations --  |t Background --  |t Systems with Unmodelled Variables --  |t Types of Variables --  |t Structural Form, Reduced Form, Final Form --  |t Models with Rational Expectations --  |t Cointegrated Variables --  |t Estimation --  |t Stationary Variables --  |t Estimation of Models with I(1) Variables --  |t Remarks on Model Specification and Model Checking --  |t Forecasting --  |t Unconditional and Conditional Forecasts --  |t Forecasting Estimated Dynamic SEMs --  |t Multiplier Analysis --  |t Optimal Control --  |t Concluding Remarks on Dynamic SEMs --  |g Part IV.  |t Infinite Order Vector Autoregressive Processes --  |g 11.  |t Vector Autoregressive Moving Average Processes --  |t Finite Order Moving Average Processes --  |t VARMA Processes --  |t The Pure MA and Pure VAR Representations of a VARMA Process --  |t A VAR(1) Representation of a VARMA Process --  |t The Autocovariances and Autocorrelations of a VARMA(p, q) Process --  |t Forecasting VARMA Processes -- 
505 8 0 |t Transforming and Aggregating VARMA Processes --  |t Linear Transformations of VARMA Processes --  |t Aggregation of VARMA Processes --  |t Interpretation of VARMA Models --  |t Granger-Causality --  |t Impulse Response Analysis --  |g 12.  |t Estimation of VARMA Models --  |t The Identification Problem --  |t Nonuniqueness of VARMA Representations --  |t Final Equations Form and Echelon Form --  |t Illustrations --  |t The Gaussian Likelihood Function --  |t The Likelihood Function of an MA(1) Process --  |t The MA(q) Case --  |t The VARMA(1, 1) Case --  |t The General VARMA(p, q) Case --  |t Computation of the ML Estimates --  |t The Normal Equations --  |t Optimization Algorithms --  |t The Information Matrix --  |t Preliminary Estimation --  |t An Illustration --  |t Asymptotic Properties of the ML Estimators --  |t Theoretical Results --  |t A Real Data Example --  |t Forecasting Estimated VARMA Processes --  |t Estimated Impulse Responses --  |g 13.  |t Specification and Checking the Adequacy of VARMA Models --  |t Specification of the Final Equations Form --  |t A Specification Procedure -- 
505 8 0 |t Specification of Echelon Forms --  |t A Procedure for Small Systems --  |t A Full Search Procedure Based on Linear Least Squares Computations --  |t Hannan-Kavalieris Procedure --  |t Poskitt's Procedure --  |t Remarks on Other Specification Strategies for VARMA Models --  |t Model Checking --  |t LM Tests --  |t Residual Autocorrelations and Portmanteau Tests --  |t Prediction Tests for Structural Change --  |t Critique of VARMA Model Fitting --  |g 14.  |t Cointegrated VARMA Processes --  |t The VARMA Framework for I(1) Variables --  |t Levels VARMA Models --  |t The Reverse Echelon Form --  |t The Error Correction Echelon Form --  |t Estimation --  |t Estimation of ARMARE Models --  |t Estimation of EC-ARMARE Models --  |t Specification of EC-ARMARE Models --  |t Specification of Kronecker Indices --  |t Specification of the Cointegrating Rank --  |t Forecasting Cointegrated VARMA Processes --  |t Algebraic Exercises --  |t Numerical Exercises --  |g 15.  |t Fitting Finite Order VAR Models to Infinite Order Processes --  |t Background -- 
505 8 0 |t Multivariate Least Squares Estimation --  |t Forecasting --  |t Theoretical Results --  |t Impulse Response Analysis and Forecast Error Variance Decompositions --  |t Asymptotic Theory --  |t Cointegrated Infinite Order VARs --  |t The Model Setup --  |t Estimation --  |t Testing for the Cointegrating Rank --  |g Part V.  |t Time Series Topics --  |g 16.  |t Multivariate ARCH and GARCH Models --  |t Background --  |t Univariate GARCH Models --  |t Definitions --  |t Forecasting --  |t Multivariate GARCH Models --  |t Multivariate ARCH --  |t MGARCH --  |t Other Multivariate ARCH and GARCH Models --  |t Estimation --  |t Theory --  |t Checking MGARCH Models --  |t ARCH-LM and ARCH-Portmanteau Tests --  |t LM and Portmanteau Tests for Remaining ARCH --  |t Other Diagnostic Tests --  |t Interpreting GARCH Models --  |t Causality in Variance --  |t Conditional Moment Profiles and Generalized Impulse Responses --  |t Problems and Extensions -- 
505 8 0 |g 17.  |t Periodic VAR Processes and Intervention Models --  |t The VAR(p) Model with Time Varying Coefficients --  |t General Properties --  |t ML Estimation --  |t Periodic Processes --  |t A VAR Representation with Time Invariant Coefficients --  |t ML Estimation and Testing for Time Varying Coefficients --  |t Bibliographical Notes and Extensions --  |t Intervention Models --  |t Interventions in the Intercept Model --  |t A Discrete Change in the Mean --  |t An Illustrative Example --  |t Extensions and References --  |g 18.  |t State Space Models --  |t Background --  |t State Space Models --  |t The Model Setup --  |t More General State Space Models --  |t The Kalman Filter --  |t The Kalman Filter Recursions --  |t Proof of the Kalman Filter Recursions --  |t Maximum Likelihood Estimation of State Space Models --  |t The Log-Likelihood Function --  |t The Identification Problem --  |t Maximization of the Log-Likelihood Function --  |t Asymptotic Properties of the ML Estimator -- 
505 8 0 |t A Real Data Example --  |g Appendices  |g A.  |t Vectors and Matrices --  |t Basic Definitions --  |t Basic Matrix Operations --  |t The Determinant --  |t The Inverse, the Adjoint, and Generalized Inverses --  |t Inverse and Adjoint of a Square Matrix --  |t Generalized Inverses --  |t The Rank --  |t Eigenvalues and -vectors - Characteristic Values and Vectors --  |t The Trace --  |t Some Special Matrices and Vectors --  |t Idempotent and Nilpotent Matrices --  |t Orthogonal Matrices and Vectors and Orthogonal Complements --  |t Definite Matrices and Quadratic Forms --  |t Decomposition and Diagonalization of Matrices --  |t The Jordan Canonical Form --  |t Decomposition of Symmetric Matrices --  |t The Choleski Decomposition of a Positive Definite Matrix --  |t Partitioned Matrices --  |t The Kronecker Product --  |t The vec and vech Operators and Related Matrices --  |t The Operators --  |t Elimination, Duplication, and Commutation Matrices --  |t Vector and Matrix Differentiation -- 
505 8 0 |t Optimization of Vector Functions --  |t Problems --  |g B.  |t Multivariate Normal and Related Distributions --  |t Multivariate Normal Distributions --  |t Related Distributions --  |g C.  |t Stochastic Convergence and Asymptotic Distributions --  |t Concepts of Stochastic Convergence --  |t Order in Probability --  |t Infinite Sums of Random Variables --  |t Laws of Large Numbers and Central Limit Theorems --  |t Standard Asymptotic Properties of Estimators and Test Statistics --  |t Maximum Likelihood Estimation --  |t Likelihood Ratio, Lagrange Multiplier, and Wald Tests --  |t Unit Root Asymptotics --  |t Univariate Processes --  |t Multivariate Processes --  |g D.  |t Evaluating Properties of Estimators and Test Statistics by Simulation and Resampling Techniques --  |t Simulating a Multiple Time Series with VAR Generation Process --  |t Evaluating Distributions of Functions of Multiple Time Series by Simulation --  |t Resampling Methods. 
520 |a Deals with analyzing and forecasting multiple time series, considering a range of models and methods. This reference work and graduate-level textbook enables readers to perform their analyses in a competent manner. 
588 |a Description based on print version record. 
650 0 |a Time-series analysis.  |0 http://id.loc.gov/authorities/subjects/sh85135430 
650 7 |a MATHEMATICS  |x Probability & Statistics  |x Time Series.  |2 bisacsh 
650 1 7 |a Tijdreeksen.  |2 gtt 
650 7 |a Analise De Series Temporais.  |2 larpcal 
655 4 |a Electronic books. 
650 7 |a Time-series analysis.  |2 fast  |0 http://id.worldcat.org/fast/fst01151190 
776 0 8 |i Print version:  |a Lütkepohl, Helmut.  |t New introduction to multiple time series analysis.  |d Berlin : New York : Springer, 2005  |z 3540401725  |z 9783540401728  |w (DLC) 2005927322  |w (OCoLC)61028971 
856 4 0 |z SpringerLink  |u http://dx.doi.org/10.1007/3-540-27752-8 
903 |a HeVa 
929 |a eresource 
999 f f |i 0c7b152f-5940-5ee9-a6ee-f049dd89231f  |s bd609877-ec61-5512-8085-f935e01caaa4 
928 |t Library of Congress classification  |a QA280 .L87 2005eb  |l Online  |c UC-FullText  |n SpringerLink  |u http://dx.doi.org/10.1007/3-540-27752-8  |g ebooks  |i 7161828 

Similar Items