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003			IT-RoAPU
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008			210830t20201994nju fo d z eng d
020			_a9780691218632 _qPDF
024	7		_a10.1515/9780691218632 _2doi
035			_a(DE-B1597)9780691218632
035			_a(DE-B1597)567570
035			_a(OCoLC)1229161176
040			_aDE-B1597 _beng _cDE-B1597 _erda
050		4	_aQA280 _b.H264 1994
072		7	_aBUS036000 _2bisacsh
082	0	4	_a519.5/5 _220
084			_aonline - DeGruyter
100	1		_aHamilton, James Douglas _eautore
245	1	0	_aTime Series Analysis / _cJames Douglas Hamilton.
264		1	_aPrinceton, NJ : _bPrinceton University Press, _c[2020]
264		4	_c©1994
300			_a1 online resource (816 p.)
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0	0	_tFrontmatter -- _tContents -- _tPreface -- _t1 Difference Equations -- _t1.1. First-Order Difference Equations -- _t1.2. pth-Order Difference Equations -- _tAPPENDIX I.A. Proofs of Chapter 1 Propositions -- _tChapter 1 References -- _t2 Lag Operators -- _t2.1. Introduction -- _t2.2. First-Order Difference Equations -- _t2.3. Second-Order Difference Equations -- _t2.4. pth-Order Difference Equations -- _t2.5. Initial Conditions and Unbounded Sequences -- _tChapter 2 References -- _t3 Stationary ARMA Processes -- _t3.1. Expectations, Stationarity, and Ergodicity -- _t3.2. White Noise -- _t3.3. Moving Average Processes -- _t3.4. Autoregressive Processes -- _t3.5. Mixed Autoregressive Moving Average Processes -- _t3.6. The Autocovariance-Generating Function -- _t3.7. Invertibility -- _tAPPENDIX 3.A. Convergence Results for Infinite-Order Moving Average Processes -- _tChapter 3 Exercises -- _tChapter 3 References -- _t4 Forecasting -- _t4.1. Principles of Forecasting -- _t4.2. Forecasts Based on an Infinite Number of Observations -- _t4.3. Forecasts Based on a Finite Number of Observations -- _t4.4. The Triangular Factorization of a Positive Definite Symmetric Matrix -- _t4.5. Updating a Linear Projection -- _t4.6. Optimal Forecasts for Gaussian Processes -- _t4.7. Sums of ARM A Processes -- _t4.8. Wold's Decomposition and the Box-Jenkins Modeling Philosophy -- _tAPPENDIX 4.A. Parallel Between OLS Regression and Linear Projection -- _tAPPENDIX 4.B. Triangular Factorization of the Covariance Matrix for an MA(1) Process -- _tChapter 4 Exercises -- _tChapter 4 References -- _t5 Maximum Likelihood Estimation -- _t5.1. Introduction -- _t5.2. The Likelihood Function for a Gaussian AR(7J Process -- _t5.3. The Likelihood Function for a Gaussian AR(p) Process -- _t5.4. The Likelihood Function for a Gaussian MA(1) Process -- _t5.5. The Likelihood Function for a Gaussian MA(q) Process -- _t5.6. The Likelihood Function for a Gaussian ARMA(p, q) Process -- _t5.7. Numerical Optimization -- _t5.8. Statistical Inference with Maximum Likelihood Estimation -- _t5.9. Inequality Constraints -- _tAPPENDIX 5. A. Proofs of Chapter 5 Propositions -- _tChapter 5 Exercises -- _tChapter 5 References -- _t6 Spectral Analysis -- _t6.1. The Population Spectrum -- _t6.2. The Sample Periodogram -- _t6.3. Estimating the Population Spectrum -- _t6.4. Uses of Spectral Analysis -- _tAPPENDIX 6. A. Proofs of Chapter 6 Propositions -- _tChapter 6 Exercises -- _tChapter 6 References -- _t7 Asymptotic Distribution Theory -- _t7.1. Review of Asymptotic Distribution Theory -- _t7.2. Limit Theorems for Serially Dependent Observations -- _tAPPENDIX 7.A. Proofs of Chapter 7 Propositions -- _tChapter 7 Exercises -- _tChapter 7 Exercises -- _t8 Linear Regression Models -- _t8.1. Review of Ordinary Least Squares with Deterministic Regressors and i.i.d. Gaussian Disturbances -- _t8.2. Ordinary Least Squares Under More General Conditions -- _t8.3. Generalized Least Squares -- _tAPPENDIX 8. A. Proofs of Chapter 8 Propositions -- _tChapter 8 Exercises -- _tChapter 8 References -- _t9 Linear Systems of Simultaneous Equations -- _t9.1. Simultaneous Equations Bias -- _t9.2. Instrumental Variables and Two-Stage Least Squares -- _t9.3. Identification -- _t9.4. Full-Information Maximum Likelihood Estimation -- _t9.5 Estimation Based on the Reduced Form -- _t9.6. Overview of Simultaneous Equations Bias -- _tAPPENDIX 9.A. Proofs of Chapter 9 Proposition -- _tChapter 9 Exercise -- _tChapter 9 References -- _t10 Covariance-Stationary Vector Processes -- _t10.1. Introduction to Vector Autoregressions -- _t10.2. Autocovariances and Convergence Results for Vector Processes -- _t10.3. The Autocovariance-Generating Function for Vector Processes -- _t10.4. The Spectrum for Vector Processes -- _t10.5. The Sample Mean of a Vector Process -- _tAPPENDIX 10.A. Proofs of Chapter 10 Propositions -- _tChapter 10 Exercises -- _tChapter 10 References -- _t11 Vector Autoregressions -- _t11.1. Maximum Likelihood Estimation and Hypothesis Testing for an Unrestricted Vector Autoregression -- _t11.2. Bivariate Granger Causality Tests -- _t11.3. Maximum Likelihood Estimation of Restricted Vector Autoregressions -- _t11.4. The Impulse-Response Function -- _t11.5. Variance Decomposition -- _t11.6. Vector Autoregressions and Structural Econometric Models -- _t11.7. Standard Errors for Impulse-Response Functions -- _tAPPENDIX 11. A. Proofs of Chapter 11 Propositions -- _tAPPENDIX 11.B. Calculation of Analytic Derivatives -- _tChapter 11 Exercises -- _tChapter 11 References -- _t12 Bayesian Analysis -- _t12.1. Introduction to Bayesian Analysis -- _t12.2. Bayesian Analysis of Vector Autoregressions -- _t12.3. Numerical Bayesian Methods -- _tAPPENDIX 12.A. Proofs of Chapter 12 Propositions -- _tChapter 12 Exercise -- _tChapter 12 References -- _t13 The Kalman Filter -- _t13.1. The State-Space Representation of a Dynamic System -- _t13.2. Derivation of the Kalman Filter -- _t13.3. Forecasts Based on the State-Space Representation -- _t13.4. Maximum Likelihood Estimation -- _t13.5. The Steady-State Kalman Filter -- _t13.6. Smoothing -- _t13.7. Statistical Inference with the Kalman Filter -- _t13.8. Time-Varying Parameters -- _tAPPENDIX 13. A. Proofs of Chapter 13 Propositions -- _tChapter 13 Exercises -- _tChapter 13 References -- _t14 Generalized Method of Moments -- _t14.1. Estimation by the Generalized Method of Moments -- _t14.2. Examples -- _t14.3. Extensions -- _t14.4. GMM and Maximum Likelihood Estimation -- _tAPPENDIX 14. A. Proof of Chapter 14 Proposition -- _tChapter 14 Exercise -- _tChapter 14 References -- _t15 Models of Nonstationary Time Series -- _t15.1. Introduction -- _t15.2. Why Linear Time Trends and Unit Roots? -- _t15.3. Comparison of Trend-Stationary and Unit Root Processes -- _t15.4. The Meaning of Tests for Unit Roots -- _t15.5. Other Approaches to Trended Time Series -- _tAPPENDIX 15. A. Derivation of Selected Equations for Chapter 15 -- _tChapter 15 References -- _t16 Processes with Deterministic Time Trends -- _t16.1. Asymptotic Distribution of OLS Estimates of the Simple Time Trend Model -- _t16.2. Hypothesis Testing for the Simple Time Trend Model -- _t16.3. Asymptotic Inference for an Autoregressive Process Around a Deterministic Time Trend -- _tAPPENDIX 16. A. Derivation of Selected Equations for Chapter 16 -- _tChapter 16 Exercises -- _tChapter 16 References -- _t17 Univariate Processes with Unit Roots -- _t17.1. Introduction -- _t17.2. Brownian Motion -- _t17.3. The Functional Central Limit Theorem -- _t17.4. Asymptotic Properties of a First-Order Autoregression when the True Coefficient Is Unity -- _t17.5. Asymptotic Results for Unit Root Processes with General Serial Correlation -- _t17.6. Phillips-Perron Tests for Unit Roots -- _t17.7. Asymptotic Properties of a pth-Order Autoregression and the Augmented Dickey-Fuller Tests for Unit Roots -- _t17.8. Other Approaches to Testing for Unit Roots -- _t17.9. Bayesian Analysis and Unit Roots -- _tAPPENDIX 17.A. Proofs of Chapter 17 Propositions -- _tChapter 17 Exercises -- _tChapter 17 References -- _t18 Unit Roots in Multivariate Time Series -- _t18.1. Asymptotic Results for Nonstationary Vector Processes -- _t18.2. Vector Autoregressions Containing Unit Roots -- _t18.3. Spurious Regressions -- _tAPPENDIX 18.A. Proofs of Chapter 18 Propositions -- _tChapter 18 Exercises -- _tChapter 18 References -- _t19 Cointegration -- _t19.1. Introduction -- _t19.2. Testing the Null Hypothesis -- _t19.3. Testing Hypotheses About the Cointegrating Vector -- _tAPPENDIX 19. A. Proofs of Chapter 19 Propositions -- _tChapter 19 Exercises -- _tChapter 19 References -- _t20 Full-Information Maximum Likelihood Analysis of Cointegrated Systems -- _t20.1. Canonical Correlation -- _t20.2. Maximum Likelihood Estimation -- _t20.3. Hypothesis Testing -- _t20.4. Overview of Unit Roots-To Difference or Not to Difference? -- _tAPPENDIX 20.A. Proof of Chapter 20 Proposition -- _tChapter 20 Exercises -- _tChapter 20 References -- _t21 Time Series Models of Heteroskedasticity -- _t21.1. Autoregressive Conditional Heteroskedasticity (ARCH) -- _t21.2. Extensions -- _tAPPENDIX 21. A. Derivation of Selected Equations for Chapter 21 -- _tChapter 21 References -- _t22 Modeling Time Series with Changes in Regime -- _t22.1. Introduction -- _t22.2. Markov Chains -- _t22.3. Statistical Analysis of i.i.d. Mixture Distributions -- _t22.4.
505	0	0	_tTime Series Models of Changes in Regime -- _tAPPENDIX 22. A. Derivation of Selected Equations for Chapter 22 -- _tChapter 22 Exercise -- _tChapter 22 Reference -- _tA Mathematical Review -- _tA.1. Trigonometry -- _tA.2. Complex Numbers -- _tA.3. Calculus -- _tA.4. Matrix Algebra -- _tA.5. Probability and Statistics -- _tAppendix A References -- _tB Statistical Tables -- _tC Answers to Selected Exercises -- _tD Greek Letters and Mathematical Symbols Used in the Text -- _tAuthor Index -- _tSubject Index
506	0		_arestricted access _uhttp://purl.org/coar/access_right/c_16ec _fonline access with authorization _2star
520			_aThe last decade has brought dramatic changes in the way that researchers analyze economic and financial time series. This book synthesizes these recent advances and makes them accessible to first-year graduate students. James Hamilton provides the first adequate text-book treatments of important innovations such as vector autoregressions, generalized method of moments, the economic and statistical consequences of unit roots, time-varying variances, and nonlinear time series models. In addition, he presents basic tools for analyzing dynamic systems (including linear representations, autocovariance generating functions, spectral analysis, and the Kalman filter) in a way that integrates economic theory with the practical difficulties of analyzing and interpreting real-world data. Time Series Analysis fills an important need for a textbook that integrates economic theory, econometrics, and new results. The book is intended to provide students and researchers with a self-contained survey of time series analysis. It starts from first principles and should be readily accessible to any beginning graduate student, while it is also intended to serve as a reference book for researchers.
538			_aMode of access: Internet via World Wide Web.
546			_aIn English.
588	0		_aDescription based on online resource; title from PDF title page (publisher's Web site, viewed 30. Aug 2021)
650		0	_aTime-series analysis.
650		7	_aBUSINESS & ECONOMICS / Investments & Securities / General. _2bisacsh
653			_aAbsolute summability.
653			_aAutocovariance.
653			_aBartlett kernel.
653			_aBlock exogeneity.
653			_aCointegrating vector.
653			_aConsumption spending.
653			_aCospectrum.
653			_aDickey-Fuller test.
653			_aEM algorithm.
653			_aExchange rates.
653			_aFilters.
653			_aFundamental innovation.
653			_aGamma distribution.
653			_aGlobal identification.
653			_aGross national product.
653			_aHessian matrix.
653			_aInequality constraints.
653			_aInvertibility.
653			_aJacobian matrix.
653			_aJoint density.
653			_aKhinchine's theorem.
653			_aKronecker product.
653			_aLagrange multiplier.
653			_aLoss function.
653			_aMean-value theorem.
653			_aMixingales.
653			_aMonte Carlo method.
653			_aNewton-Raphson.
653			_aOrder in probability.
653			_aOrthogonal.
653			_aPermanent income.
653			_aQuadrature spectrum.
653			_aRecessions.
653			_aReduced form.
653			_aSample periodogram.
653			_aStock prices.
653			_aTaylor series.
653			_aVech operator.
850			_aIT-RoAPU
856	4	0	_uhttps://doi.org/10.1515/9780691218632?locatt=mode:legacy
856	4	0	_uhttps://www.degruyter.com/isbn/9780691218632
856	4	2	_3Cover _uhttps://www.degruyter.com/cover/covers/9780691218632.jpg
942			_cEB
999			_c195223 _d195223