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Hamilton J.D. — Time Series Analysis
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Íàçâàíèå: Time Series Analysis
Àâòîð: Hamilton J.D.
Àííîòàöèÿ: The 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.
ßçûê:
Ðóáðèêà: Ìàòåìàòèêà /
Ñòàòóñ ïðåäìåòíîãî óêàçàòåëÿ: Ãîòîâ óêàçàòåëü ñ íîìåðàìè ñòðàíèö
ed2k: ed2k stats
Ãîä èçäàíèÿ: 1994
Êîëè÷åñòâî ñòðàíèö: 820
Äîáàâëåíà â êàòàëîã: 26.02.2008
Îïåðàöèè: Ïîëîæèòü íà ïîëêó |
Ñêîïèðîâàòü ññûëêó äëÿ ôîðóìà | Ñêîïèðîâàòü ID
Ïðåäìåòíûé óêàçàòåëü
Lewis, Craig M. 672
Li, W.K. 127
Liitkepohl, Helmut 336 339
Likelihood function 746—747. See also Maximum likelihood estimation (MLE)
Likelihood function, concentrating 638
Likelihood function, vector autoregression and 291—294 310—311
Likelihood ratio test 144—145 296—298 648—650
Lilien, David M. 667 672
LIMIT See Convergence
Linear dependence 728—729
Linear dependence, Geweke's measure of 313—314
Linear projection, forecasts and 74—76 92—100
Linear projection, multivariate 75
Linear projection, ordinary least squares regression and 75—76 113—114
Linear projection, properties of 74—75
Linear projection, updating 94
Linear regression See also Generalized least squares (GLS); Generalized method of moments (GMM); Ordinary least squares (OLS)
Linear regression, algebra of 200—202
Linear regression, review of OLS and i.i.d. 200—207
Linearly deterministic 109
Linearly indeterministic 109
Litterman, Robert B. 360—62 402—3
Ljungqvist, Lars 305 447 653
Lo, Andrew W. 449 531 532
Local identification 334 388
Local maximum 134 137 226
Logarithms 717—718
Long-run effect 6—7
Loretan, Mico 608 613
Loss function 72
Lucas, Robert E., Jr. 306
MA See Moving average
MacKinlay, A.Craig 531 532
Maddala, G.S. 250
Magnus, Jan R. 302 317 318 704
Makov, U.E. 689
Malinvaud, E. 411
Malliaris, A.G. 479
Mankiw, N. Gregory 361 444
Mark, Nelson 664
Markov chain 678
Markov chain, absorbing state 680
Markov chain, ergodic 681—682
Markov chain, forecasting 680
Markov chain, periodic 685
Markov chain, reducible 680
Markov chain, transition matrix 679
Markov chain, two-state 683—84
Markov chain, vector autoregressive representation 679
Marsden, Jerrold E. 196 704
Martin, R.D. 127
Martingale difference sequence 189—190 193—195
Matrix / matrices, adjoint 727
Matrix / matrices, conjugate transposes 734—735
Matrix / matrices, determinant 724—727
Matrix / matrices, diagonal 721
Matrix / matrices, duplication 301
Matrix / matrices, gain 380
Matrix / matrices, geometric series 732
Matrix / matrices, Hessian 139 736
Matrix / matrices, idempotent 201
Matrix / matrices, identity 722
Matrix / matrices, information 143—144 429
Matrix / matrices, inverse 727—728
Matrix / matrices, Jacobian 737
Matrix / matrices, Jordan decomposition 730—731
Matrix / matrices, lower triangular 725
Matrix / matrices, nonsingular 728
Matrix / matrices, optimal weighting 412—414
Matrix / matrices, partitioned 724
Matrix / matrices, positive definite 733—734
Matrix / matrices, positive semide&nite 733
Matrix / matrices, power of 722
Matrix / matrices, singular 728
Matrix / matrices, square 721
Matrix / matrices, symmetric 723
Matrix / matrices, trace of 723—724
Matrix / matrices, transition 679
Matrix / matrices, transposition 723
Matrix / matrices, triangular 729
Matrix / matrices, triangular factorization 87
Matrix / matrices, upper triangular 727
Maximum likelihood estimation (MLE) 117 747.
Maximum likelihood estimation (MLE), asymptotic properties of 142—145 429—430
Maximum likelihood estimation (MLE), concentrated 638
Maximum likelihood estimation (MLE), conditional 122 125—126
Maximum likelihood estimation (MLE), EM algorithm and 688—689
Maximum likelihood estimation (MLE), full-information maximum likelihood 247—250
Maximum likelihood estimation (MLE), Gaussian AR process and 118—127
Maximum likelihood estimation (MLE), Gaussian ARMA process and 132—133
Maximum likelihood estimation (MLE), Gaussian MA process and 127—131
Maximum likelihood estimation (MLE), general coefficient constraints and 315—318
Maximum likelihood estimation (MLE), global maximum 134 137
Maximum likelihood estimation (MLE), GLS and 222
Maximum likelihood estimation (MLE), GMM and 427—431
Maximum likelihood estimation (MLE), Kalman filter and 385—389
Maximum likelihood estimation (MLE), local 134 137
Maximum likelihood estimation (MLE), prediction error decomposition 122 129
Maximum likelihood estimation (MLE), regularity conditions 427 698
Maximum likelihood estimation (MLE), standard errors for 143—144 429—430
Maximum likelihood estimation (MLE), statistical inference with 142—145
Maximum likelihood estimation (MLE), vector autoregression and 291—302 309—318
Maximum likelihood estimation (MLE), Wald test for 429—430
McCurdy, Thomas H. 691
McLeod, A.I. 127
Mean square, convergence in 182—183 749
Mean squared error (MSE) 73
Mean squared error (MSE), of linear projection 74 75 77
Mean, ergodic for the 47
Mean, population 739
Mean, sample 186—195 279—285 740—741
Mean, unconditional 44
Mean-value theorem 196
Meese, Richard 305
Milh0j, Anders 662 670
Miller, H.D. 681 685
Mixingales 190—192
Mixture distribution 685—689
MLE See Maximum likelihood estimation (MLE)
Modulus 709
Moments See also Generalized method of moments (GMM)
Moments, population 739—740 744—745
Moments, posterior 363—365
Moments, sample 740—741
Moments, second 45 92—95 192—193
Monahan, J.Christopher 284 285 618
Money demand 1 324
Monfort, A. 431 670
Monte Carlo method 216 337 365—366 398
Moore, John B. 47 373 403
Mosconi, Rocco 650
Moving average (MA), cointegration and 574—575
Moving average (MA), first order 48—49
Moving average (MA), forecasting 82—83 95—98
Moving average (MA), infinite order 51—52
Moving average (MA), maximum likelihood estimation for Gaussian 127—131 387
Moving average (MA), parameter estimation 132 387
Moving average (MA), population spectrum for 154—155 276
Moving average (MA), qth order 50—51
Moving average (MA), sums of 102—107
Moving average (MA), vector 262—264
MSE See Mean squared error (MSE)
Mustafa, C. 672
Muth, John F. 440
Nason, James A. 361
Nelson, Charles R. 109 253 426 444 504
Nelson, Daniel B. 662 666 667 668 672
Nelson, Harold L. 126
Nerlove, Mark 671
Neudecker, Heinz 302 704
Newbold, Paul 557
Newey — West estimator 220 281—282
Newey, Whitney K. 220 281—82 284 414
Newton-Raphson 138—139
Ng, Victor K. 668 671 672
Nicholls, D.F. 218
Noh, Jason 672
Nonparametric estimation See also Kernel
Nonparametric estimation, bandwidth 165 671
Nonparametric estimation, conditional variance and 671
Nonparametric estimation, cross validation 671
Nonparametric estimation, population spectrum 165—167
Nonsingular 728
Nonstochastic 739
Normal distribution 745—746 748—749 751—752
Normalization, cointegration and 589
Numerical optimization, convergence criterion 134 137
Numerical optimization, Davidon — Fletcher — Powell 139—142
Numerical optimization, EM algorithm 688—689 696
Numerical optimization, grid search 133—134
Numerical optimization, inequality constraints 146—148
Numerical optimization, Newton — Raphson 138—139
Numerical optimization, numerical maximization 133 146
Numerical optimization, numerical minimization 142
Numerical optimization, steepest ascent 134—137
O'Nan, Michael 203 704
Observation equation 373
Ogaki, Masao 424 573 575 618 651
Ohanian, Lee E. 554
Oil prices, effects of 307—308
OLS See Ordinary least squares
Operators, annihilation 78
Operators, first-difference 436
Operators, time series 25—26
Option prices 672
Order in probability 460
Ordinary least squares (OLS) See also Generalized least squares (GLS); Hypothesis tests; Regression
Ordinary least squares (OLS), algebra of 75—76 200—202
Ordinary least squares (OLS), autocorrelated disturbances 217 282—283
Ordinary least squares (OLS), chi-square test 213
Ordinary least squares (OLS), distribution theory 209 432—433
Ordinary least squares (OLS), estimated coefficient vector 202—203
Ordinary least squares (OLS), F test 205—207
Ordinary least squares (OLS), GMM and 416—418
Ordinary least squares (OLS), heteroskedasticity 217 282—283
Ordinary least squares (OLS), linear projection and 75—76 113—114
Ordinary least squares (OLS), non-Gaussian disturbances 209
Ordinary least squares (OLS), t test 204 205
Ordinary least squares (OLS), time trends and 454—460
Orthogonal 743
Orthogonality conditions 411
Orthogonalized impulse-response function 322
Ouliaris, Sam 573 593 601 630
Outer-product estimate 143
Pagan, A.R. 218 389 668 671 672
Pantula, S.G. 532 670
Park, Joon Y. 214 483 532 547 549 573 575 601 618 651
Partial autocorrelation, population 111—112
Partial autocorrelation, sample 111—112
Parzen kernel 283
Pearce, Douglas K. 305
Period 708
Periodic 707
Periodic, Markov chain 685
Periodogram, multivariate 272—275
Periodogram, univariate 158—163
Permanent income 440
Perron, Pierre 449 475 506—16
Phase 275 708
Phillips triangular representation 576—578
Phillips — Ouliaris — Hansen tests 599
Phillips — Perron tests 506—514 762—763
Phillips, G.D. A. 386
Phillips, Peter C.B. 195 214 475 483 487 506—516 532 533 534 545 547 549 554 557 576—578 587 593 601 608 613—618 630 650 651
Pierce, David A. 305
Plim 181 749
Ploberger, Werner 698
Plosser, Charles I. 444 573
Polar coordinates 704—705 710
Polynomial m lag operator 27 258
Population, canonical correlations 630—633
Population, coherence 275
Population, correlation 743
Population, covariance 742
Population, moments 739—740 744—745
Population, spectrum 61—62 152—157 163—167 269 276—277
Port, Sidney C. 749
Porter-Hudak, Susan 449
Posterior density 352
Powell, M.J.D. 139—42
Power series 714
PRECISION 355
Predetermined 238
Prediction error decomposition 122 129 310
Present value 4 19—20
Principal diagonal 721
Prior distribution 351
Probability limit 181 749
pth-order autoregressive process 58—59
pth-order difference equations 7—20 33—36
Pukkila, Tarmo 133
Purchasing power parity See Exchange rates
qth-order moving average 50—51
Quadratic equations 710—711
Quadratic spectral kernel 284
Quadrature spectrum 271
Quah, Danny 335
Quandt, Richard E. 142
Quasi-maximum likelihood estimate 126 145 430—431
Quasi-maximum likelihood estimate, ARCH 663—664
Quasi-maximum likelihood estimate, GLS 222
Quasi-maximum likelihood estimate, GMM and 430—431
Quasi-maximum likelihood estimate, Kalman filter and 389
Quasi-maximum likelihood estimate, standard errors 145
RADIANS 704
Ramage, J.G. 216
Random variable 739
Random walk 436. See also Unit root process OLS estimation 486—504
Rao, C R. 52 183 184 204
Rappoport, Peter 449
Rational expectations 422
Rational expectations, efficient markets hypothesis 306
Raymond, Jennie 664
Real interest rate 376
Real number 708
Recessions 167—168 307—308 450 697—698
Recursive substitution 1—2
Reduced form 245—246 250—252
Reduced form, VAR 327 329
Reducible Markov chain 680
Regime-switching models, Bayesian estimation 689
Regime-switching models, derivation of equations 692—693
Regime-switching models, description of 690—691
Regime-switching models, EM algorithm 696
Regime-switching models, maximum likelihood 692 695—696
Regime-switching models, singularity 689
Regime-switching models, smoothed inference and forecasts 694—695
Regression See also Generalized least squares (GLS); Generalized method of moments (GMM); Ordinary least squares (OLS)
Regression, classical assumptions 202
Regression, time-varying parameters 400
Regularity conditions 427 698
Reichlin, Lucrezia 449
Reinsel, G.C. 601 630
Residual sum of squares (RSS) 200
Rich, Robert W. 664
Ridge regression 355
Rissanen, J. 133
Robins, Russell P. 667 672
Rogers, John H. 677
Rothenberg, Thomas J. 247 250 334 362 388 411
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