references abraham, b. and j. ledolter (1983), statistical methods for...
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REFERENCES
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Abraham, B.
Ali, M.M.
Amemiya, T.
Anderson, B.D.O.
Ansley, C.F.
Arato, M.
Astrom, K.J.
Balestra, P.
Beach, C.M.
Bertsekas, D.P.
Box, G.E.P.
Carvalho, J.L.
Chow, G.C.
Cramer, J.S.
Deist1er, M.
Dhrymes, P.J.
Domowitz, I.
Duncan, D.B.
Durbin, J.
Friedman, A.
Fuller, W.A.
Godfrey, L.G.
Goldberg, S.
Goodwin, G.C.
Gooijer, J.G. de
Grether, D.M.
AUTHOR INDEX
93, 177
7, 183
8, 131, 132, 137, 177
56, 74, 76, 177
2, 8, 11, 12, 25, 34, 47, 51, 54, 59, 60, 177
180
2, 25, 177
56, 64, 74, 76, 177
7, 24, 25, 26, 177
11, 177
68, 76, 177
20, 33, 36, 57, 172, 177, 180
181
56, 62, 74, 76, 134, 178
153, 178
3, 56, 76, 78, 79, 179
133, 178
171, 172, 183
3, 72, 178
8, 19, 137, 178
42, 178
131, 132, 137, 177
5, 159, 160, 161, 170-175, 178
98, 178
56, 74, 76, 178
8, 25, 178
181
186
Griffiths, W.E.
Gupta, Y.P.
Hamilton, J.D.
Hannan, E.J.
Harvey, A.C.
Hatanaka, M.
Hill, R.C.
Horn, S.D.
Jenkins, G.M.
Jones, R.H.
Judge, G.G.
Kailath, T.
Kalman, R.E.
Kapteyn, A.
Kashyap, A.K.
Kedem, G.
Kiviet, J.F.
Klein, L.R.
Kmenta, J.
Knottnerus, P.
Kohn, R.
Koyck, L.M.
Ledo1ter, J.
Lee, T.C.
Levinson, N.
Liviatan, N.
Ljung, G.M.
179
131, 132, 178
95, 179
3, 56, 76, 78, 79, 179
2, 3, 8, 55-57, 59, 66, 72, 73, 75, 85
88-93, 98, 109, 114, 117, 123, 124, 131,
134, 137, 138, 143, 158, 179, 181
137, 141, 179
179
3, 73, 178
20, 33, 57, 172, 177
123, 179
100, 130, 131, 152, 179
56, 67, 76, 124, 179
56, 67, 74, 76, 179
3, 97, 98, 100, 107, 108, 110, 183
4, 159, 160, 170, 174, 181
54, 181
113, 175, 179
131, 132, 138, 148, 152, 180
130, 139, 152, 181
4, 159, 160, 164, 180
2, 34, 180
131, 132, 134, 149, 180
93, 177
179
8, 19, 180
130, 180
33, 36, 180
MacKinnon, J.G.
Magnus, J .R.
McAvinchey, I.D.
McLeod, A. I.
Me1ard, G.
Mentz, R.P.
Moore, J.B.
Ner1ove, M.
Neudecker, H.
Oberhofer, W.
Palm, F.C.
Pesaran, M.H.
Phadke, M.S.
Phillips, G.D.A.
Pierse, R.G.
Porter, R.D.
Richardson, S.M.
Sargan, J.D.
Savin, N.E.
Schmidt, P.
Shaman, P.
Shilov, G.E.
Sin, K.S.
Sneek, J.M.
Spanos, A.
Steyn, I.J.
Stoer, J.
Theil, H.
Thursby, J.G.
11, 177
67, 110, 180
109, 143, 179
2, 34, 37, 180
55, 180
27, 28, 180
56, 74, 76, 177
27, 28, 128, 181
67, 180
130, 139, 181
170, 181
7,181
54, 181
187
2, 8, 55-57, 59, 75, 85, 88-93, 124, 179,181
123, 179
4, 159, 160, 170, 174, 181
97, 181
159, 161, 181
97, 159, 181, 182
139, 161, 182
8, 182
42, 182
56, 74, 76, 178
170, 181
62, 182
85, 182
18, 182
12, 73, 116, 137, 156, 182
4, 159-168, 170-174, 182
188
Tiao, G.C.
Wansbeek, T.J.
White, H.
White, K.J.
Zinde-Wa1sh, V.
7, 183
3, 97, 98, 100, 107, 108, 110, 183
171, 172, 183
97, 159, 181, 182
2, 8, 38, 183
SUBJECT INDEX
absolute convergence adaptive expectations addition rule adjustments angle Ansley's algorithm approximate method autocorre1ated disturbances,
general
autoregressive (AR) process, general estimation,
dynamic models with missing observations
stationarity conditions tests
autoregressive-moving average (ARMA) process, general Ansley's algorithm autocovariance matrix dynamic models GLS recursions GLS transformation state space model tests
moving average (MA) process, general Ansley's algorithm GLS recursions GLS transformation Koyck transformation ML estimation state space model
autocovariances, general AR process ARMA process MA process
autocovariance matrix, general AR process and misssing data ARMA process MA process
backward shift operator Banach space band matrix bandwidth basis of a linear space
149 128, 134 42 98, 127, 129, 138, 143 43 8, 51, 54 56, 88, 93
1, 4, 5, 97, 108, 112, 133, 159, 161, 162, 165, 168-171, 174
3,7,19,40,57,65
137, 141, 146 97, 98, 100, 101, 107-110 124 112, 113, 119, 120, 159-167, 170, 172-175
2-9, 18-20, 22, 55, 56, 59, 127 51,52 32, 38 137-140, 143, 148, 152 47, 50 38, 40-42 66, 67, 85, 88-90, 123 160, 172, 173, 175
2, 7-9, 23, 37, 38, 47 51-54 47 22, 24-27, 29 131, 134 145 55, 56, 88, 93
52, 172 99, 109 20, 33, 34, 41 26
1, 2, 5, 7-9, 52, 54, 88, 124, 137 107, 108 32, 38, 42, 53, 55 23, 25, 27
33, 128 see space see matrix 51, 100, 101 12, 14, 16, 17
190
Bayesian interpretation Berndt, Hall, Hall, and Hausman
algorithm best linear unbiased estimator bias boundary conditions Box-Jenkins equations
Cauchy product Cauchy-Schwarz inequality Cauchy sequence chi-square distribution (X2) Cho1esky decomposition Cho1esky root Cochrane-Orcutt procedure cofactor complex valued conditional expectations conditional normal distribution conditional variances conjugate complex consistent
convergence of iterations correlation
correlation coefficient cosine (cos) covariance,
general covariance stationarity
covariance matrix
Cramer-Rao inequality
degrees of freedom De Moivre's theorem density function dependence of tests derivative determinant (1.1) difference equation differential operator (D) distributed lag model disturbance double precision dual problem Durbin's h-test Durbin's S-test Durbin-Watson test dynamic models
efficiency eigenvalue error covariance matrix error process
81
158 60, 131 28, 33
149 5, 6 43
62,
32,
64, 67, 68
36, 37, 44, 45
112, 114, 116, 162, 171 8, 10, 19, 47, 51-54, 59, 144 9, 25 109, 130, 162 23 30, 43, 49, 150, 151 73-75 I, 55, 60 63, 64 125 110, 127, 129-134, 136-142, 144, 148, 151, 152, 165, 172, 175 130, 139, 147 128, 130; see also autocorre1ated disturbances 153, 166, 168 28, 30, 31, 43, 49, 150, 151, 154
147, 154; see also autocovariances 9 10, 59, 61, 115, 124, 130, 132, 142, 144, 155, 171; see also autocovariance matrix I, 4, 5, 6, 81, 127, 143, 153, 156
161 30, 150 57, 58, 82, 85 163 110, 111, 114-117, 119, 121, 141-147, 153 8, II, 12, 16, 21-23, 40-42, 47, 51, 54, 58-60, 92, 108, 133 27, 30, 32, 36 114-116, 142, 143, 154-158 4, 127, 129 see auto correlated disturbances 88, 93 8,12 173,174 162, 163, 165, 168, 170, 174, 175 97, 159, 163, 164, 170, 173, 174 127, 138, 148, 151
110, 127, 129-133, 137-138, 140-144, 147, 151 12, 125 56, 64, 67, 68, 78, 81, 87, 88 see autocorre1ated disturbances
error term errors in variables error variance estimation error Euclidean distance Euclidean space exogenous
first order condition flow variable forecasts forward shift operator F-test functional form
Gaussian noise Gauss-Newton algorithm generalized least squares (GLS) geometrically declining lag
coefficients geometric derivation geometric distributed lag geometric interpretation geometric series Gram-Schmidt orthogonalization grid (search)
Hessian heteroskedasticity consistent Hilbert space homogeneous hyperparallelepiped hyperplane hypothesis,
general null hypothesis (HO)
idempotent identity matrix imaginary value inconsistent independent,
general linearly stochastically
independently and identically normally distributed (iid)
induction inefficient information matrix initial estimate initial matrix initial values inner product inner product space innovations
67, 173 131 63, 93-95 62, 69 12 see space 98, 172
110, 157 94 56, 57 89 117, 122, 161, 170, 172, 175 159, 160; see also misspecification
83 4, 6, 137, 138, 143, 146-148 3, 7-8, 55, 73, 86, 90, 99, 109, 137, 174-175
127, 128 55, 67, 73 127, 148 1, 21, 55, 61, 64, 70, 79, 153, 156 149 6, 8, 14 111, 132, 133, 151, 167
see matrix 172 see space 27, 32, 36 21, 22 61
161, 173 112, 114, 116, 119, 161-163, 171, 175; see also tests
see matrix see matrix 174; see also complex valued 120, 130, 165, 169
13, 22, 65, 81, 158, 169, 170 13 13, 57, 61, 71, 83
22, 29, 32, 85, 92, 98, 164 see mathematical induction 139 see matrix 56, 73, 81, 130 124 67, 85, 86, 138, 141, 143, 147 12, 43, 153, 154, 156 see space 68, 70, 75, 82, 103
192
insignificant instrumental variables (IV) iterative procedure
Jacobian
joint distribution joint significance
Kalman filter equations, general derivations likelihood function missing observations prediction intervals
Kalman gain Kalman matrix Kalman regression equation Koyck equations Koyck model Koyck transformation Kronecker product(@) Kronecker delta (&tj)
lagged dependent variable lag polynomial Lagrange multiplier (LM) test,
general adjusted for missing data modified (MLM)
lag structure Laplace's expansion theorem least squares estimation,
nonlinear least squares estimation
ordinary least squares (OLS), general, OLS estimation
OLS residuals recursive OLS estimation
least squares sense least squares solution length Levinson-Durbin (LD) algorithm likelihood function,
general Ansley's algorithm dynamic models missing observations models with ARMA disturbances multivariate distribution
likelihood ratio (LR) test
linear least squares estimate (LLSE)
linear models
112, 113, 161, 165, 170, 173 130, 151, 152 130, 139, 147, 151, 158
2, 11, 32, 40-42, 58, 82, 92, 108, 117-119, 133, 142 57 113, 122
1-4, 6, 8, 55, 56, 61, 64, 65, 81, 82, 98 67, 70, 72, 75, 78 85, 86, 92, 93 123, 124 93 3, 70, 72, 92 see matrix 80 132, 134 149 4, 128, 152 11, 12, 42, 87 40, 65
137, 148, 160, 173-175 see polynomial
4, 98, 114, 115, 172 120, 123 113, 117-120, 122, 123, 163, 175 148, 151 12
12, 139, 151
1, 99, 139, 142, 145, 164 4, 7, 8, 11, 26, 109, 129-136, 141, 143, 144, 146, 165, 168, 169, 171 109, 113, 118, 122, 133, 135, 163, 165, 172 66 62, 67, 79 14, 104, 155 see norm 19, 20, 38
2, 9, 11, 12, 55, 56, 154, 158 25, 51 131, 142, 146 110, 111, 113-114, 116, 118, 124 82-85, 90, 92 57-59 4, 97, 107, 108, 112, 113, 124, 162, 163, 165, 169, 172, 173
67; see also least squares estimation 1
linear unbiased estimator linear vector space
Markov scheme mathematical induction matrix.
Hessian idempotent identity matrix information matrix inverse Kalman matrix lower triangular transformation
matrix nonsingular partitioned regression matrix singular Toepli tz matrix
maximum likelihood (ML)
measurement equation measurement noise metric space minimum error variance minimum variance estimator missing observations
misspecification (MS) Monte Carlo experiments
moving average (MA) process multiple correlation
coefficient (R) multiplication rule multiplicity multivariate analysis
Newton-Raphson algorithm nonlinear equations nonlinear least squares
estimation nonlinear regression nonlinear restrictions nonnegative definite nonstationarity norm (11.11) normal distribution normal equations
null hypothesis (HO) numerical (in)stabi1ity
one step ahead prediction error one step ahead predictor ordered hypotheses ordinary least squares (OLS)
67 see space
1. 97. 124. 130 23. 67. 89
142 116 10 142. 157 8. 18. 21. 25. 31. 36. 144. 157 61-64. 155
see transformation 9. 10, 12, 13, 21. 31, 61 31, 36. 60 69 73, 88, 158 9. 19. 27 2. 8, 10, 11, 55, 56, 74, 90, 92, 93, 97, 108, 110-112, 132, 133, 138, 146-148, 173 65 65, 67, 84, 86 see space 63 60, 64, 81 3, 97, 98, 100, 101, 108, 110, 112, 113, 118, 119, 120 4, 159, 170, 171
193
4, 56, 88, 93, 108, 162-164, 166, 167, 170, 171 see auto correlated disturbances
153, 156, 157 42 30, 48, 49 55-57, 60, 64, 80-82, 95
158 131, 134
see least squares estimation 139 162, 164, 165, 168, 170 124 20, 38 12-15, 17, 21, 41, 43, 44, 153-156 I, 9, 12, 22, 57, 63-65, 75 14, 27, 45, 61, 69, 70, 73, 80, 104, 132, 155,169 see hypothesis 18, 20, 21, 38
68, 80, 82, 88, 91-93, 124 73 160 see least squares estimation
194
orthogonal orthogonal remainder orthonormal orthonormal basis orthonorma1ization
paradox partial adjustment parti tioned perpendicular polynomial,
characteristic lag polynomial
positive definite positive semidefinite posterior power powerful Prais-Winsten procedure predetermined prediction prediction interval prediction stage prior probability limit (p1im) projection Pythagorean illustration Pythagorean theorem
quadratic equation quadratic form
R2 random walk rank real number field regressand regression model regression specification error
test (RESET) regressor
regularity conditions remainder residuals,
general OLS residuals recursive
residual vector restrictions Riccati equation robust roots of
characteristic polynomials lag polynomials
rounding errors
12, 14, 53, 71, 76, 78, 61, 69, 71, 80, 81, 83, 39, 40 14, 16, 17 8, 13, 14
120 129 see matrix 13, 21, 41, 103
125 32, 41, 57, 88, 152 54, 73, 88 67, 155 81 98, 120, 123, 165, 169 161, 164 97, 108, 143 115, 141, 147, 170 93,94 56, 57, 93, 94 67 67,81
83, 104-106, 103, 155
135, 136, 165, 166, 168-170 13, 16, 39, 41, 61, 76, 103, 155 6 4,6,17,127,153,156
132, 136 9, 60
153, 156, 157 66 116 42 80 19, 32, 41, 47, 55, 56
4, 161, 163, 170, 171, 174, 175
154, 155
71, 80, 83, 141, 144, 146, 147, 157, 160, 170, 171 63, 154 139; see also orthogonal remainder
133, 137, 139, 151, 165, 166, 170, 175 see least squares estimation 91 60, 143, 154 139, 171 72 161, 171, 174
27, 30, 48, 49, 125 37,57,88,149 38, 57
sample, size small
scalar, general product variable
seasonal, autocorrelation pattern process
seemingly unrelated regression equations (SURE)
serial correlation significance level significant simulation sine (sin) singular Slutzky's theorem space,
general
Banach space Euclidean space Hilbert space inner product space linear vector space metric space subspace
spectral analysis square root algorithm standard deviation starting values state space model state vector stationarity,
general conditions covariance
stationary steady state solution stochastic stochastic error variable subspace supply function system equation system noise
Taylor series expansion t-distribution tests,
general procedure statistic strategy
time varying parameters
98, 164, 165 138, 143, 160, 174, 175
84, 88, 90 43 1, 72
164, 166, 173 5, 172 41
7, 9, 10, 12, 18
195
see auto correlated disturbances 113, 163, 175 112, 113, 159, 163 109 28, 30, 31, 49, 150, 151 see matrix 165
1, 12-14, 21, 42, 55, 61, 68, 69, 76, 104, 153, 155 43 1, 43, 156 43 43 12, 43 13 13, 16, 17, 39, 41, 53, 103, 104 38 93, 124 12 3, 93; see also initial values 2, 55, 65-67, 73, 74, 76, 85, 86, 92, 93, 123 56, 65
85, 87, 125 98, 124, 125, 148 9 22, 32, 55, 59, 90, 98 147 12, 13, 55, 104, 153, 154, 156 68 see space 128 65, 89 65
4, 122, 138-140, 143, 145, 151, 153, 157 94; see also t-test
112, 159, 161, 163, 165, 166, 171-173 97, 164, 165, 168, 173, 174 112-115, 159, 161, 165, 170, 171, 173, 174 1, 159, 160 65, 82
196
Toeplitz matrix trace (tr) transformation,
general data first order autocorrelation transformation matrix,
general lower triangular
transition matrix trend trigonometric formulae truncation parameter truncation remainder t-test two step estimator
unbiased unit circle unit vector univariate update stage
variance ratio vec(.) vector function vector space volume (V)
Wa1d test white noise
Yule-Walker equations
zero vector zig-zag procedure
see matrix 63, 64, 67, 155, 156
7, 50, 58, 82, 92, 101, 109, 118, 174, 175 7, 26, 47, 49, 52, 97, 103 1, 97, 133, 161, 169
8, 11, 18, 19, 24, 32, 33, 38, 42, 50, 91 10, 15, 16, 20, 40, 47, 51, 54, 90, 100, 102, 109, 144 125 147 49 172 132, 148, 149 117, 118, 163; see also t-distribution 97, 108, 127
67, 154 57, 88, 125, 149 86 93 68, 79
72 82, 87 64 see space 21, 22, 40, 41
172 66, 79, 107, 117, 119, 129, 130, 134, 137, 140, 161-163
19, 109, 166
42 139
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J.C. WUlems (Ed.)
From Data to Model 1989. vn, 246 pp. 35 figs. 10 tabs. Hardcover DM 98,- ISBN 3-540-51571-2
This book consists of 5 chapters. The general theme is to develop a mathematical framework and a language for modelling dynamical systems from observed data. Two chapters study the statistical aspects of approximate linear time-series analysis. One chapter develops worst case aspects of system identification. Finally, there are two chapters on system approximation. The first one is a tutorial on the Hankel-norm approximation as an approach to model simplification in linear systems. The second one gives a philosophy for setting up numerical algorithms from which a model optimally fits an observed time series.
P.Hackl (Ed.)
Statistical Analysis and Forecasting of Economic Structural Change 1989. XIX, 488 pp. 98 figs. 60 tabs. Hardcover DM 178,- ISBN 3-540-51454-6
This book treats methods and problems of the statistical analysis of economic data in the context of structural change. It documents the state of the art, gives insights into existing methods, and describes new developments and trends. An introductory chapter gives a survey of the book and puts the following chapters into a broader context. The rest of the volume is organized in three parts: a) Identification of Structural Change; b) Model Building in the Presence of Structural Change; c) Data Analysis and Modeling.
Springer-Verlag
C.D.Aliprantis, D.J.Brown, O.Burkinshaw
Existence and Optimality of Competitive Equilibria 1989. XII, 284 pp. 38 figs. Hardcover DM 110,- ISBN 3-540-50811-2
Contents: The Arrow-Debreu Model. - Riesz Spaces of Commodities and Prices. - Markets with Infinitely Many Commodities. - Production with Infinitely Many Commodities. -The Overlapping Generations Model. -References. - Index.
B.L.Golden, E.A. Wasil, P. T.Harker (Eds.)
The Analytic Hierarchy Process Applications and Studies
With contributions by numerous experts
1989. VI, 265 pp. 60 figs. 74 tabs. Hardcover DM 110,- ISBN 3-540-51440-6
The book is divided into three sections. In the first section, a detailed tutorial and an extensive annotated bibliography serve to introduce the methodology. The second section includes two papers which present new methodological advances in the theory of the AHP. The third section, by far the largest, is dedicated to applications and case studies; it contains twelve chapters. Papers dealing with project selection, electric utility planning, governmental decision making, medical decision making, conflict analysis, strategic planning, and others are used to illustrate how to successfully apply the AHP. Thus, this book should serve as a useful text in courses dealing with decision making as well as a valuable reference for those involved in the application of decision analysis techniques.
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