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008 | 950301s1995 enka b 001 0 eng d | |

010 | ▼a 95154097 | |

015 | ▼a GB95-34438 | |

020 | ▼a 0198283172 | |

020 | ▼a 0198283164 (pbk) | |

040 | ▼a FrCpGE ▼b fre ▼c DLC ▼d UKM ▼d CUI ▼d FFG ▼d NYP | |

042 | ▼a lccopycat | |

049 | ▼a ACCL | |

050 | 0 0 | ▼a HB141 ▼b .H458 1995 |

082 | 0 0 | ▼a 330/.01/5195 ▼2 20 |

090 | ▼a 330.015195 ▼b H498d | |

100 | 1 | ▼a Hendry, David F. |

245 | 0 0 | ▼a Dynamic econometrics / ▼c David F. Hendry. |

260 | ▼a Oxford ; ▼a New York : ▼b Oxford University Press, ▼c 1995. | |

300 | ▼a xxxiv, 869 p. : ▼b ill. ; ▼c 24 cm. | |

440 | 0 | ▼a Advanced texts in econometrics. |

504 | ▼a Includes bibliographical references (p. 819-845) and indexes. | |

650 | 0 | ▼a Econometric models. |

653 | 0 | ▼a Econometric models |

### Holdings Information

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No. 1 | Location Main Library/Western Books/ | Call Number 330.015195 H498d | Accession No. 111060373 | Availability Available | Due Date | Make a Reservation | Service |

### Contents information

#### Table of Contents

CONTENTS List of Figures = xxiii List of Tables = xxvii Preface = xxix Ⅰ Concepts, Models, and Processes in Econometrics = 1 1 Introduction = 3 1.1 Empirical econometrics modelling = 3 1.2 The problems of econometrics = 5 1.3 The aims of this book = 6 1.4 Constructive and destructive approaches = 7 1.5 A brief discourse on scientific method = 9 1.6 Theories, instruments, and evidence = 11 1.7 Economic analysis and statistical theory = 13 1.8 Four levels of knowledge = 16 1.8.1 Probability theory = 16 1.8.2 Estimation theory = 16 1.8.3 Modelling theory = 17 1.8.4 Forecasting theory = 17 1.8.5 The origins of the methodological crisis = 18 1.9 Some economic time series = 19 1.10 A first data-generation process = 21 1.11 Empirical models as derived entities = 27 1.12 Exercises = 29 2 Econometrics Concepts = 31 2.1 Parameter = 31 2.2 Constancy = 32 2.3 Structure = 33 2.4 Distributional shape = 34 2.5 Identification and observational equivalence = 36 2.6 Interdependence = 37 2.7 Stochastic process = 38 2.8 Conditioning = 39 2.9 White noise = 39 2.10 Autocorrelation = 40 2.11 Stationarity = 42 2.12 Integratedness = 43 2.13 Co-integration = 44 2.14 Trend = 44 2.15 Heteroscedasticity = 45 2.16 Dimensionality = 46 2.17 Aggregation = 46 2.18 Marginalization = 48 2.19 A general formulation = 49 2.20 A static solved example = 51 2.21 Models, mechanisms, and DGPs = 55 2.22 Factorizations = 56 2.23 Innovation processes = 58 2.24 Empirical models = 60 2.25 White noise and innovations = 63 2.26 Auto-correlated. shocks = 64 2.27 Sequential factorization = 65 2.28 Model design = 67 2.29 A dynamic solved example = 68 2.30 Exercises = 72 3 Econometrics Tools and Techniques = 75 3.1 Review = 75 3.2 Estimating unknown parameters = 76 3.2.1 An empirical example = 76 3.2.2 Recursive estimation = 78 3.2.3 A solved recursive example = 82 3.3 Methods for evaluating models = 85 3.4 Statistical theory = 86 3.5 Asymptotic distribution theory = 87 3.6 Monte Carlo = 87 3.6.1 Distribution sampling = 88 3.6.2 Antithetic variate = 90 3.6.3 Control variables = 93 3.6.4 Response surfaces = 94 3.6.5 Invariance = 95 3.6.6 Recursive Monte Carlo = 97 3.7 Ergodicity = 98 3.8 Non-stationarity * = 100 3.9 A solved example * = 104 3.10 Vector Brownian motion * = 111 3.11 A Monte Carlo study = 115 3.12 Exercises = 118 4 Dynamics and Interdependence = 122 4.1 Nonsense regressions = 122 4.2 Analysing nonsense regressions * = 130 4.3 Spurious de-trending * = 133 4.4 First-order autoregressive dynamics = 134 4.5 Reduction and dynamics = 138 4.6 Interdependence = 140 4.7 Co-integration = 143 4.8 Bivariate dynamics = 143 4.9 A solved example = 145 4.10 Exercises = 150 5 Exogeneity and Causality = 156 5.1 What are 'exogenous variables'? = 156 5.2 Two counter-examples = 157 5.2.1 An expectations counter-example = 158 5.2.2 A fixed-regressor counter-example = 161 5.3 Weak exogeneity = 162 5.4 A cobweb model = 164 5.5 The counter-examples reconsidered = 166 5.6 An ambiguity in strict exogeneity = 167 5.7 Can the model mis-specification be detected? = 168 5.7.1 ζ t is not an innovation relative to X t-1 = 169 5.7.2 tζ t is white noise = 169 5.8 Strong exogeneity = 170 5.9 Super exogeneity = 172 5.10 An illustration of super exogeneity = 173 5.11 Causality = 175 5.11.1 Granger non-causality = 175 5.11.2 Invariance under interventions = 176 5.12 Two solved examples = 177 5.13 Weak exogeneity and unit roots * = 181 5.13.1 A bivariate cointegrated system = 182 5.13.2 Six cases of interest = 183 5.13.3 Limiting distributions = 184 5.13.4 Inference = 187 5.13.5 A Monte Carlo study = 188 5.14 Exercises = 191 6 Interpreting Linear Models = 195 6.1 Four interpretations of Y t = β 'Z t + ε t = 195 6.1.1 Regression = 196 6.1.2 Linear least-squares approximation = 197 6.1.3 Contingent plan = 198 6.1.4 Behavioral model = 200 6.2 Expectations formation = 202 6.2.1 Rational expectations = 202 6.2.2 Unbiased expectations = 204 6.2.3 Data-based expectations = 205 6.3 Autocorrelation corrections = 207 6.4 A simple dynamic model : AD(l, ,l) = 211 6.5 Lags and their measurement = 212 6.5.1 Static solutions = 212 6.5.2 Lag distributions = 214 6.5.3 Interpreting empirical lag distributions = 217 6.6 A Monte Carlo study of the AD(l, 1) model = 219 6.6.1 Coefficient biases = 220 6.6.2 Coefficient standard errors = 221 6.6.3 Parameter constancy tests = 223 6.7 An empirical illustration = 223 6.8 A solved example = 227 6.9 Exercises = 229 7 A Typology of Linear Dynamic Equations = 231 7.1 Introduction = 231 7.2 Static regression = 233 7.3 Auto-regression = 241 7.4 Differenced-data model = 247 7.5 Leading indicator = 252 7.6 Partial adjustment = 256 7.7 Common factor = 266 7.8 Finite distributed lags = 273 7.9 Dead-start models = 283 7.10 Equilibrium correction = 286 7.10.1 Co-integration = 288 7.10.2 Servo-mechanistic control = 290 7.10.3 Empirical success of ECMs = 291 7.11 Solved examples = 294 7.11.1 Partial adjustment and equilibrium correction = 294 7.11.2 Testing co-integration = 297 7.11.3 Co-integration representations = 303 7.12 Summary and conclusion = 304 7.12.1 Goodness of fit = 304 7.12.2 Distributed-lag shapes = 304 7.13 Exercises = 306 8 Dynamic Systems = 309 8.1 Introduction = 309 8.2 Statistical formulation = 310 8.3 Theoretical formulation = 311 8.4 Closed linear systems = 313 8.4.1 Formulation = 313 8.4.2 Invariance under linear transformations = 313 8.4.3 Co-integration = 315 8.5 Open linear systems = 316 8.5.1 Conditional systems = 316 8.5.2 Simultaneous equations = 317 8.5.3 Co-integration = 317 8.6 Modelling dynamic systems = 318 8.6.1 The economy is a system = 319 8.6.2 To test marginalization = 319 8.6.3 Simultaneity = 319 8.6.4 To test weak exogeneity = 320 8.6.5 To check identification = 320 8.6.6 Co-integration is a system property = 320 8.6.7 To test cross-equation dependencies = 320 8.6.8 To investigate system dynamics = 321 8.7 A typology of open linear dynamic systems = 321 8.7.1 Vector equilibrium-correction system = 322 8.7.2 Static system = 323 8.7.3 Vector auto-regression = 323 8.7.4 .Differenced. system = 323 8.7.5 Leading indicator = 323 8.7.6 Partial adjustment = 324 8.7.7 Common factor = 324 8.7.8 Finite distributed lag = 324 8.7.19 Dead start = 324 8.7.10 An empirical illustration = 325 8.8 Models of linear systems = 329 8.8.1 Vector autoregressive representation = 329 8.8.2 Vector equilibrium correction = 330 8.8.3 Simultaneous-equations model = 331 8.8.4 Conditional model = 332 8.8.5 Conditional simultaneous model = 333 8.8.6 Causal chain = 333 8.8.7 Block-recursive representation = 334 8.8.8 Triangular representation = 335 8.8.9 Empirical illustrations = 335 8.9 Analysing dynamic systems = 337 8.9.1 Dynamic multipliers = 339 8.9.2 Final forms = 340 8.9.3 Non-linearity = 340 8.9.4 Dynamic simulation = 341 8.10 Exercises = 343 9 The Theory of Reduction = 344 9.1 Introduction = 344 9.2 Data transformations and aggregation = 346 9.3 Parameters of interest = 347 9.4 Data partition = 350 9.5 Marginalization = 350 9.6 Sequential factorization = 352 9.6.1 Sequential factorization of W 1 T = 352 9.6.2 Marginalizing with respect to V T 1 = 352 9.7 Mapping to 1(0) = 353 9.8 Conditional factorization = 354 9.9 Constancy = 355 9.10 Lag truncation = 356 9.11 Functional form = 356 9.12 The derived model = 357 9.13 Econometrics concepts as measures of no information loss = 359 9.14 Implicit model design = 361 9.15 Explicit model design = 361 9.16 A taxonomy of evaluation information = 362 9.16.1 Past data = 363 9.16.2 Present data = 363 9.16.3 Future data = 363 9.16.4 Theory information = 363 9.16.5 Measurement information = 364 9.16.6 Rival models = 364 9.17 Exercises = 367 Ⅱ Statistical Tools for Econometrics Analysis = 369 10 Likelihood = 371 10.1 Review of Part Ⅰ = 371 10.2 The statistical model = 373 10.3 Estimation criteria and estimation methods = 374 10.4 The likelihood function = 377 10.5 Maximum likelihood estimation = 378 10.6 Properties of the score = 379 10.7 Properties of maximum likelihood estimators = 382 10.8 Large-sample properties of MLEs = 384 10.9 Two solved examples = 387 10.10 Misleading inference when V ≠ X - 1 = 391 10.11 Derived distributions = 393 10.12 Asymptotic equivalence = 394 10.13 Concentrated likelihood functions = 395 10.14 Marginal and conditional distributions = 396 10.15 Estimator generating equations = 397 10.16 An EG E for common-factor dynamics = 398 10.17 Exercises = 399 11 Simultaneous Equations Systems = 405 11.1 Introduction = 405 11.2 The statistical system = 406 11.3 System dynamic specification = 407 11.4 System estimation = 409 11.5 System co-integration estimation = 412 11.6 System evaluation = 418 11.7 Empirical co-integration illustration = 419 11.8 The econometrics model = 421 11.9 Identification = 422 11.10 An EGE for simultaneous equations estimation = 423 11.10.1 Non-linear parameters = 428 11.10.2 Vector autoregressive errors = 429 11.11 A solved example = 430 11.12 Simultaneous equations modelling = 433 11.13 Derived statistics = 434 11.14 Empirical model estimates = 436 11.15 Exercises = 440 12 Measurement Problems in Econometrics = 442 12.1 Introduction = 442 12.2 Errors in variables = 443 12.2.1 Analysing the errors-Lin-variables model = 445 12.2.2 Instrumental variables = 449 12.3 Dynamic latent-variables models = 451 12.3.1 Method of simulated moments = 453 12.3.2 Simulated likelihood functions = 455 12.3.3 A dynamic min-condition model = 456 12.3.4 Exogeneity issues = 458 12.4 Revisions to 1(1) data = 459 12.4.1 Revisions to price indices = 459 12.4.2 An empirical illustration = 461 12.5 The impact of measurement errors on ECMs = 463 12.6 Exercises = 466 13 Testing and Evaluation = 468 13.1 The statistical framework = 468 13.2 Non-central X 2 distributions = 470 13.3 Large-sample properties of tests = 472 13.4 Understanding the non-central X 2 distribution = 474 13.5 Test power = 476 13.6 Likelihood-ratio, Wald, and Lagrange-multiplier tests = 477 13.6.1 Likelihood-ratio tests = 478 13.6.2 Wald tests = 479 13.6.3 Lagrange-multiplier or efficient-score tests = 480 13.7 Comparing the tests = 480 13.8 A solved example = 482 13.9 Non-linear restrictions = 485 13.10 Some methodological considerations = 487 13.10.1 The null hypothesis = 487 13.10.2 The alternative hypothesis = 488 13.10.3 The test statistics = 488 13.10.4 The significance level = 490 13.10.5 Multiple testing = 490 13.11 Exercises = 492 Ⅲ Empirical Modelling = 499 14 Encompassing = 501 14.1 Introduction = 501 14.2 Augmenting the conventional testing strategy = 503 14.2.1 Rejection is not final = 503 14.2.2 Corroboration is not definitive = 504 14.3 Encompassing and mis-specification analysis = 505 14.4 Formalizing encompassing = 506 14.5 Levels of analysis = 509 14.5.1 Specification encompassing = 510 14.5.2 Mis-specification encompassing = 510 14.5.3 Selection encompassing = 510 14.6 Parsimonious encompassing = 511 14.7 A simple example = 512 14.7.1 Can M 1 ? M 2 ? = 513 14.7.2 Can M 2 ? M 1 ? = 514 14.8 Nesting and encompassing = 514 14.9 Encompassing in linear regression = 516 14.10 Encompassing in stationary stochastic processes = 520 14.11 A solved example = 523 14.12 An empirical illustration = 525 14.13 Encompassing the VAR = 527 14.14 Testing the Lucas critique529 14.15 The applicability of the critique = 531 14.16 Tests for. super exogeneity = 532 14.17 Encompassing implications of feedback versus feed forward models = 534 14.17.1 Does H b ? H e ? = 535 14.17.2 Does H e ? H b ? = 535 14.17.3 Incomplete information = 536 14.17.4 Implications = 536 14.18 Empirical testing of invariance = 536 14.18.1 Testing super-exogeneity = 537 14.18.2 Testing encompassing = 538 14.19 Exercises = 539 15 Modelling Issues = 544 15.1 Data mining = 544 15.2 Theory dependence versus sample dependence = 546 15.2.1 Theory-driven approaches = 546 15.2.2 Data-driven approaches = 547 15.2.3 Bayesian approaches = 547 15.2.4 Data modelling using economic theory guidelines = 547 15.3 Progressive research strategies = 550 15.3.1 Identified cointegration vector = 550 15.3.2 Orthogonal parameters = 552 15.3.3 Inappropriate estimation = 553 15.3.4 Common trends = 553 15.3.5 Residual analysis = 553 15.3.6 Expectations and structure = 553 15.4 Pyrrho's lemma = 554 15.5 Dummy variables = 557 15.6 Seasonal adjustment = 559 15.6.1 Seasonal filters = 561 15.6.2 Properties of seasonal filters = 562 15.6.3 Co-integration = 563 15.7 Approximating moving-average processes = 565 15.8 A solved example : modelling second moments = 568 15.9 Populations and samples = 574 15.10 Exercises = 576 16 Econometrics in Action = 577 16.1 The transactions demand for money = 577 16.2 Economic theories of money demand = 579 16.3 Econometrics formulation = 581 16.4 Financial innovation = 583 16.5 Data description = 585 16.6 A small monetary system = 591 16.7 Cointegration analysis = 597 16.8 Modelling the Ⅰ(0) PVAR = 600 16.9 Evaluating the model = 604 16.10 A single-equation money-demand model = 606 16.11 Transformation and reduction = 611 16.12 Post-modelling evaluation = 614 16.12.1 Learning adjustment = 615 16.12.2 Constancy = 615 16.12.3 Encompassing = 616 16.13 Testing the Lucas critique = 616 16.14 Post-sample evaluation = 618 16.15 Policy implications = 618 16.16 Data definitions = 619 Ⅳ Appendices = 621 Al Matrix Algebra = 623 A1.1 Summary of the appendix chapters = 623 A1.2 Matrices = 625 A1.3 Matrix operations = 627 Al.4 Relations between operations = 633 A1.5 Partitioned inverse = 634 A1.6 Polynomial matrices = 635 A2 Probability and Distributions = 639 A2.1 Introduction = 639 A2.1.1 Chance = 639 A2.1.2 Empirical distributions and histograms = 639 A2.2 Events = 641 A2.2.1 Random experiments, sets, and sample spaces = 641 A2.2.2 Complements, unions, and intersections = 642 A2.2.3 Event space = 644 A2.2.4 Measurability = 646 A2.3 Probability = 646 A2.3.1 Probability spaces = 647 A2.3.2 Conditional probability = 648 A2.3.3 Stochastic independence = 651 A2.4 Random variables = 653 A2.4.1 Mapping events to numbers = 653 A2.4.2 Image sets = 654 A2.4.3 Functions of random variables = 655 A2.5 Distribution and density functions = 656 A2.5.1 Cumulative distribution function = 656 A2.5.2 Density function = 656 A2.5.3 Change of variable = 657 A2.5.4 Normal distribution = 657 A2.5.5 Parameters, probability models, and distributions = 658 A2.6 Joint distributions = 659 A2.6.1 Joint distribution functions = 659 A2.6.2 Marginal distributions = 659 A2.6.3 Conditional distributions = 661 A2.7 Expectations = 662 A2.7.1 Expectations, moments, and correlations = 662 A2.7.2 Conditional expectations and minimum variance = 663 A2.7.3 Indicator functions = 665 A2.7.4 Chibychev's inequality = 666 A2.8 Bivariate normal distribution = 666 A2.8.1 Change of variable = 666 A2.8.2 The bivariate normal density = 667 A2.8.3 Conditional normal = 668 A2.8.4 Regression = 669 A2.9 Multivariate normal = 670 A2.9.1 Multivariate normal density = 670 A2.9.2 Multiple regression = 670 A2.9.3 Multivariate regression = 671 A2.9.4 Functions of normal variables : X 2 , t and F distributions = 672 A2.10 Exercises = 674 A3 Statistical Theory = 677 A3.1 Sampling distributions = 677 A3.1.1 Statistics = 677 A3.1.2 Derived distributions = 678 A3.1.3 χ2 , t, and F distributions = 680 A3.1.4 Sufficiency = 681 A3.1.5 Estimation criteria = 683 A3.1.6 Consistency and asymptotic efficiency = 684 A3.2 Likelihood = 684 A3.2.1 Likelihood function = 684 A3.2.2 Log-likelihood = 685 A3.2.3 Estimation = 685 A3.2.4 The score and the Hessian = 686 A3.3 Maximum-likelihood estimation = 687 A3.3.1 Efficiency and the information matrix = 687 A3.3.2 Cram e r-Rao bound = 688 A3.3.3 Properties of the information matrix = 689 A3.3.4 Estimating the information matrix = 689 A3.4Statistical inference and testing = 690 A3.4.1 Null and alternative hypotheses = 690 A3.4.2 Critical regions, error types, and power = 690 A3.4.3 Significance level = 691 A3.5 Powerful tests = 692 A3.5.1 Neyman-Pearson lemma = 692 A3.5.2 Likelihood-ratio, Wald, and efficient-score tests = 693 A3.6 Non-parametric density estimation = 694 A3.7 Multiple regression = 695 A3.7.1 The multiple-regression model = 695 A3.7.2 Ordinary least squares = 696 A3.7.3 The Gauss-Markov theorem = 698 A3.7.4 Distributional results = 698 A3.7.5 Subsets of parameters = 700 A3.7.6 Partitioned inversion = 702 A3.7.7 Regression and inversion = 703 A3.7.8 Multiple correlation = 703 A3.7.9 Partial correlation = 704 A3.7.10 Maximum-likelihood estimation = 705 A3.9 Exercises = 706 A4 Asymptotic Distribution Theory = 707 A4.1 Introduction = 707 A4.2 Orders of magnitude = 710 A4.2.1 Deterministic sequences = 710 A4.2.2 Stochastic sequences = 711 A4.3 Stochastic convergence = 712 A4.4 Laws of large numbers = 714 A4.4.1 Weak law of large numbers = 714 A4.4.2 Strong law of large numbers = 714 A4.5 Central-limit theorems = 715 A4.6 Vector random variables = 718 A4.7 Solved examples = 719 A4.7.1 Example 1 : an I ID process = 719 A4.7.2 Example 2 : A trend model = 720 A4.8 Stationary dynamic processes = 722 A4.8.1 Vector autoregressive representations = 722 A4.8.2 Mann and Wald's theorem = 723 A4.8.3 Hannan's theorem = 724 A4.8.4 Limiting distribution of OLS for a linear equation = 725 A4.9 Instrumental variables = 728 A4.10 Mixing processes = 730 A4.10.1 Mixing and ergodicity = 730 A4.10.2 Uniform mixing and α-mixing processes = 731 A4.10.3 Laws of large numbers and central-limit theorems = 732 4.11 Martingale difference sequences = 733 A4.11.1 Constructing martingales = 733 A4.11.2 Properties of martingale-difference sequences = 734 A4.11.3 Applications to maximum-likelihood estimation = 736 A4.12 A solved autoregressive example = 738 A4.13 Ifigher-order approximations = 741 A4.13.1 Delta method = 741 A4.13.2 Asymptotic expansions = 742 A4.13.3 Power-series expansions = 744 A4.13.4 Addendum = 746 A4.14 Exercises = 748 A.5 Numerical Optimization Methods = 751 A5.1 Introduction = 751 A5.2 An overview of numerical optimization = 753 A5.3 Maximizing likelihood functions = 757 A5.4 Scalar optimization = 759 A5.4.1 Search methods = 761 A5.4.2 Gradient methods = 765 A5.5 Multivariate optimization = 767 A5.5.1 Gradient methods = 767 A5.5.2 Step-wise optimization = 771 A5.5.3 Conjugate directions = 773 A5.5.4 Variable metric or quasi-Newton methods = 776 A5.6 Conclusion = 779 A5.7 Exercises = 780 A6 Macro-Econometrics Models = 781 A6.1 Introduction = 781 A6.2 The skeletal structure of macro models = 782 A6.2.1 Modelled variables = 782 A6.2.2 Time lags = 783 A6.2.3 Error terms = 784 A6.2.4 Time aggregation = 784 A6.2.5 Interdependence = 785 A6.2.6 Size = 785 A6.2.7 The economy as a system = 785 A6.3 The national income accounts = 786 A6.3.1 Commodity flows = 786 A6.3.2 Aggregating economic transactions = 786 A6.3.3 Reconciling nominal and real magnitudes = 786 A6.4 The components of macro models = 787 A6.4.1 Kinds of variables = 787 A6.4.2 Kinds of equations = 788 A6.4.3 Behavioral equations = 789 A6.4.4 Identity equations = 789 A6.4.5 Technical equations = 790 A6.4.6 Equilibrium conditions = 790 A6.4.7 Stock-flow equations = 791 A6.4.8 Adjustment equations = 791 A6.4.9 Expectations formation = 791 A6.4.10 Observation equations = 792 A6.5 A simultaneous system of equations = 792 A6.5.1 Price dynamics = 792 A6.5.2 Simultaneity = 793 A6.6 Sectors and markets = 794 A6.6.1 Households = 795 A6.6.2 Firms = 796 A6.6.3 Static-equilibrium solutions = 798 A6.6.4 Dynamic adjustment = 800 A6.7 Additional aspects of the first model = 801 A6.7.1 Financial markets = 801 A6.7.2 Government sector = 801 A6.7.3 Foreign sector = 802 A6.7.4 Completing equations = 803 A6.7.5 Revised National Income identities = 805 A6.8 Industrial disaggregation = 805 A6.9 A general framework = 807 A6.9.1 Stable process = 809 A6.9.2 Trending process = 809 A6.9.3 Difference stable process = 809 A6.9.4 Quadratic trend process = 809 A6.9.5 Integrated process = 810 A6.9.6 Co-integrated process = 810 A6.10 Forecasting = 810 A6.10.1 Forecast standard errors = 811 A6.10.2 Model evaluation = 811 A6.11 An example = 812 A6.12 Addendum : static-model solution = 815 A6.13 Macro-model notation = 817 References = 819 Common Acronyms = 847 Glossary = 848 Author Index = 853 Subject Index = 859