Ãëàâíàÿ    Ex Libris    Êíèãè    Æóðíàëû    Ñòàòüè    Ñåðèè    Êàòàëîã    Wanted    Çàãðóçêà    ÕóäËèò    Ñïðàâêà    Ïîèñê ïî èíäåêñàì    Ïîèñê    Ôîðóì   
blank
Àâòîðèçàöèÿ

       
blank
Ïîèñê ïî óêàçàòåëÿì

blank
blank
blank
Êðàñîòà
blank
Hamilton J.D. — Time Series Analysis
Hamilton J.D. — Time Series Analysis



Îáñóäèòå êíèãó íà íàó÷íîì ôîðóìå



Íàøëè îïå÷àòêó?
Âûäåëèòå åå ìûøêîé è íàæìèòå Ctrl+Enter


Íàçâàíèå: Time Series Analysis

Àâòîð: Hamilton J.D.

Àííîòàöèÿ:

The last decade has brought dramatic changes in the way that researchers analyze time series data. This much-needed book synthesizes all of the major recent advances and develops a single, coherent presentation of the current state of the art of this increasingly important field. James Hamilton provides for the first time a thorough and detailed textbook account of important innovations such as vector autoregressions, estimation by generalized method of moments, the economic and statistical consequences of unit roots, time-varying variances, and nonlinear time series models. In addition, Hamilton presents traditional tools for analyzing dynamic systems, including linear representations, autocovariance, generating functions, spectral analysis, and the Kalman filter, illustrating their usefulness both for economic theory and for studying and interpreting real-world data. This book is intended to provide students, researchers, and forecasters with a definitive, self-contained survey of dynamic systems, econometrics, and time series analysis. Starting from first principles, Hamilton's lucid presentation makes both old and new developments accessible to first-year graduate students and nonspecialists. Moreover, the work's thoroughness and depth of coverage will make Time Series Analysis an invaluable reference for researchers at the frontiers of the field. Hamilton achieves these dual objectives by including numerous examples that illustrate exactly how the theoretical results are used and applied in practice, while relegating many details to mathematical appendixes at the end of chapters. As an intellectual roadmap of the field for students and researchers alike, this volume promises to be the authoritative guide for years to come.


ßçûê: en

Ðóáðèêà: Ìàòåìàòèêà/

Ñòàòóñ ïðåäìåòíîãî óêàçàòåëÿ: Ãîòîâ óêàçàòåëü ñ íîìåðàìè ñòðàíèö

ed2k: ed2k stats

Ãîä èçäàíèÿ: 1994

Êîëè÷åñòâî ñòðàíèö: 820

Äîáàâëåíà â êàòàëîã: 14.09.2007

Îïåðàöèè: Ïîëîæèòü íà ïîëêó | Ñêîïèðîâàòü ññûëêó äëÿ ôîðóìà | Ñêîïèðîâàòü ID
blank
Ïðåäìåòíûé óêàçàòåëü
Rogers, John H.      677
Rothenberg, Thomas J.      247 250 334 362 388 411
Rothschild, Michael      671
Royden, H.L.      191
RSS      see "Residual sum of squares"
Rubin, D.B.      387 689
Rudebusch, Glenn D.      449
Runkle, David E.      337 339 663 669 672
Ruud, Paul A.      250
Said, Said E.      530 532
Saikkonen, Pentti      608
Sample autocorrelations      110—111
Sample canonical correlations      633—635
Sample likelihood function      747
Sample mean, definition of      741
Sample mean, variance of      188 279—281
Sample moments      740—741
Sample periodogram      158—163 272—275
Sargan, J.D.      532
Sargent, Thomas J.      33 34 39 41 67 78 109 335 423
Savin, N.E.      216 488 516
scalar      721
Schmidt, Peter      513 516 532
Scholes, Myron      672
Schwert, G. William      513 516 668 672
Score      427—428
Seasonality      167—169
Second moments      45 92—95
Second moments, consistent estimation of      192—193
Second-order autoregressive process      56—58
Second-order difference equations      17 29—33
Seemingly unrelated regressions      315
Selover, David D.      573
Serial correlation      225—227
Shapiro, Matthew D.      335
Shiller, Robert J.      360 573
Shin, Y.      532
Shumway, R.H.      387
Sill, Keith      426
Simon, David P.      664
Sims — Stock — Watson scaling matrix      457
Sims — Stock — Watson transformation      464 518
Sims, Christopher A.      291 297 302 304 330 402—403 445 454 455 464 483 532—534 547 549 555
Simultaneous equations      see also "Two-stage least squares"
Simultaneous equations, bias      233—238 252—253
Simultaneous equations, estimation based on the reduced form      250—252
Simultaneous equations, full-information maximum likelihood estimation      247—250
Simultaneous equations, identification      243—247
Simultaneous equations, instrumental variables and two-stage least squares      238—243
Simultaneous equations, nonlinear systems of      421—422
Simultaneous equations, overview of      252—253
Sine      704 706—707
Singleton, Kenneth J.      422 424
singular      728
Singularity      689
Sinusoidal      706
Skew      746
Small-sample distribution      216—217 516
Smith, A.F.M.      689
Smoothing, Kalman filter and      394—397
Solo, Victor      195 483 532 545 547
Sowell, Fallaw      449 532
Spectral analysis, population spectrum      152—157 163—167 269
Spectral analysis, sample periodogram      158—163 272—275
Spectral analysis, uses of      167—172
Spectral representation theorem      157
Spectrum      see also "Kernel estimates" "Periodogram"
Spectrum, coherence      275
Spectrum, cospectrum      271—272
Spectrum, cross      270
Spectrum, estimates of      163—167 276—277 283—285
Spectrum, frequency zero and      189 283
Spectrum, gain      275
Spectrum, low-frequency      169
Spectrum, phase      275
Spectrum, population      61—62 152—157 163—167 269 276—277
Spectrum, quadrature      271
Spectrum, sample      158—163 272—275
Spectrum, sums of processes and      172
Spectrum, transfer function      278
Spectrum, vector processes and      268—278
Spurious, regression      557—562
Square summable      52
Srba, Frank      571 572 581
Standard deviation, population      740
Startz, Richard      253 427
State equation      372
State vector      372
State-space model      see "Kalman filter"
Stationary/stationarity, covariance      45—46
Stationary/stationarity, difference      444
Stationary/stationarity, strictly      46
Stationary/stationarity, trend-stationary      435
Stationary/stationarity, vector      258—259
Stationary/stationarity, weakly      45—46
Steepest ascent      134—137
Stinchcombe, Maxwell      482 698
Stochastic processes, central limit theorem for stationary      195
Stochastic processes, composite      172
Stochastic processes, expectations and      43—45
Stochastic variable      739
Stock prices      37—38 306—307 422—424 668—669 672
Stock, James H.      305 376 444 445 447 454 455 464 483 532 533 547 549 555 573 578 587 601 608 613 630 653
Stoffer, D.S.      387
Stone, Charles J.      704
Strang, Gilbert      704
Structural econometric models, vector autoregression and      324—336
Student's t distribution      see "t distribution"
Summable, absolute      52 64
Summable, square      52
Sums of ARMA processes      102—108
Sums of ARMA processes, AR      107—108
Sums of ARMA processes, autocovariance generating function of      106
Sums of ARMA processes, MA      102—107
Sums of ARMA processes, spectrum of      172
Sup operator      481
Superconsistent      460
Susmel, Raul      662
Swift, A.L.      127
t distribution      205 213 356—357 409—410 746 755
t statistic      204
Tauchen, George      426 427 671
taxes      361
Taylor series      713—714 737—738
Taylor theorem      713 737—738
Taylor, William E.      247
Theil, Henri      203 359 704
Theorem, Cramer — Wold      184
Theorem, De Moivre      153 716—717
Theorem, Gauss — Markov      203 222
Theorem, Granger representation      582
Theorem, Khinchine's      183
Theorem, Taylor      713 737—738
Thomas, George B., Jr.      157 166 704
Three-stage least squares      250
Tierney, Luke      363—365
Time domain      152
Time series operators      25—26
Time series process      43
Time trends      25 435 see
Time trends, approaches to      447—450
Time trends, asymptotic distribution of      454—460
Time trends, asymptotic inference for autoregressive process around      463—472
Time trends, breaks in      449—450
Time trends, hypothesis testing for      461—463
Time trends, linear      438
Time trends, OLS estimation      463
Time-varying parameters, Kalman filter and      398—403
Titterington, D.M.      689
Toda, H.Y.      554 650
Trace      723
Transition matrix      679
Transposition      723
Trend-stationary      435
Trend-stationary, comparison of unit root process and      438—444
Trend-stationary, forecasts for      439
Trends representation (Stock — Watson), common      578
Triangular factorization of a second-moment matrix and linear projection      92—95
Triangular factorization, block      98—100
Triangular factorization, covariance matrix and      114—115
Triangular factorization, description of      87—91
Triangular factorization, maximum likelihood estimation and      128—129
Triangular representation      576—578
Trigonometry      157 166 704—708
Trognon, A.      431
Two-stage least squares (2SLS), asymptotic distribution of      241—242
Two-stage least squares (2SLS), coefficient estimator      238
Two-stage least squares (2SLS), consistency of      240—241
Two-stage least squares (2SLS), general description of      238—239
Two-stage least squares (2SLS), GMM and      420—421
Two-stage least squares (2SLS), instrumental variable estimation      242—243
Uhlig, Harald      532—534
Unconditional density      44
Unconditional mean      44
Uncorrelated      92 743
Unidentified      244
Uniformly integrable      191
Unimodal      134
Unit circle      32 709
Unit root process      435—436 see "Dickey
Unit root process, asymptotic distribution      475—477 504—506
Unit root process, Bayesian analysis      532—534
Unit root process, Beveridge — Nelson decomposition      504 545—546
Unit root process, comparison of trend-stationary and      438—444
Unit root process, difference versus not to difference      651—653
Unit root process, dynamic multipliers      442—444
Unit root process, forecasts for      439—441
Unit root process, functional central limit theorem and      483—486
Unit root process, Johansen's test      646
Unit root process, meaning of tests for      444—447 515—516
Unit root process, multivariate asymptotic theory      544 547
Unit root process, observational equivalence      444—447 515—516
Unit root process, OLS estimation of autoregression      527
Unit root process, Phillips — Perron tests      506—514
Unit root process, small-sample distribution      516
Unit root process, spurious regression      557—562
Unit root process, variance ratio test      531—532
Unit root process, vector autoregression      549—557
van Dijk, H.K.      365
var      see "Vector autoregression"
Variance      44—45 740
Variance of sample mean      188 279—281
Variance ratio test      531—532
Variance, decomposition      323—324
Variance, population      740
Veall, Michael R.      214
Vec operator      265
Vech operator      300—301
Vector autoregression      see also "Cointegration" "Impulse-response
Vector autoregression, autocovariance generating function and      267
Vector autoregression, autocovariances and convergence results for      264—266
Vector autoregression, Bayesian analysis and      360—362
Vector autoregression, cointegration and      579—580
Vector autoregression, impulse-response function and      318—323
Vector autoregression, introduction to      257—261
Vector autoregression, likelihood ratio test      296—298
Vector autoregression, Markov chain and      679
Vector autoregression, maximum likelihood estimation and      291—302 309—318
Vector autoregression, restricted      309—318
Vector autoregression, spectrum for      276
Vector autoregression, standard errors      298 301 336—340
Vector autoregression, stationarity      259
Vector autoregression, structural econometric models and      324—336
Vector autoregression, time-varying parameters      401—403
Vector autoregression, unit roots      549—557
Vector autoregression, univariate representation      349
Vector martingale difference sequence      189
Vector processes, asymptotic results for nonstationary      544—548
Vectors, forecasting      77
Vuong, Q.H.      305
Wald form      213 299
Wald test      205 214
Wald test for maximum likelihood estimation      429—430
Wall, Kent D.      386 388
Watson, Mark W.      305 330 335 376 387 389 444 447 454 455 464 483 547 549 555 573 578 601 608 613 630 653
Wei, C.Z.      483 532
Weinbach, Gretchen C.      690 691
Weiss, Andrew A.      663
Wertz, V.      388
West, Kenneth D.      220 281—282 284 414 555 647 672
White noise, Gaussian      25 43 48
White noise, independent      48
White noise, process      47—48
White, Halbert      126 144 145 185 189 193 196 218 280 281 282 412 418 427 428 429 431 482 664 698
White, J.S.      483
White, Kenneth J.      214
Whiteman, Charles H.      39 533
Wiener process      478
Wiener — Kolmogorov prediction formula      80
Wold's decomposition      108—109
Wold's decomposition, Kalman filter and      391—394
Wold, Herman      108—109 184
Wooldridge, Jeffrey M.      431 590 591 608 663 671 672
Yeo, Stephen      571 572 581
Yoo, Byung Sam      575 596
Yule — Walker equations      59
Zellner, Arnold      315 362
Zuo, X.      672
1 2 3 4
blank
Ðåêëàìà
blank
blank
HR
@Mail.ru
       © Ýëåêòðîííàÿ áèáëèîòåêà ïîïå÷èòåëüñêîãî ñîâåòà ìåõìàòà ÌÃÓ, 2004-2024
Ýëåêòðîííàÿ áèáëèîòåêà ìåõìàòà ÌÃÓ | Valid HTML 4.01! | Valid CSS! Î ïðîåêòå