Verfügbarkeit: Econometric modelling with time series

Econometric modelling with time series: specification, estimation and testing

Gespeichert in:

Bibliographische Detailangaben
Beteiligte Personen:	Martin, Vance 1955- (VerfasserIn), Hurn, Stan 1961- (VerfasserIn), Harris, David 1969- (VerfasserIn)
Format:	Buch
Sprache:	Englisch
Veröffentlicht:	New York, NY Cambridge Univ. Press 2013
Ausgabe:	1. publ.
Schriftenreihe:	Themes in modern econometrics
Schlagwörter:	Zeitreihenanalyse Ökonometrisches Modell
Links:	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025217621&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025217621&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA
Beschreibung:	Incl. bibliogr. references and index
Umfang:	XXXV, 887 S. graph. Darst.
ISBN:	9780521196604 0521196604 9780521139816 0521139813

Internformat

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Datensatz im Suchindex

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adam_text	Contents List of Illustrations page xxi Computer Code Used in the Examples xxv Preface xxxi PART ONE Maximum Likelihood 1 The Maximum Likelihood Principle 3 1.1 Introduction 3 1.2 Motivating Examples 3 1.3 Joint Probability Distributions 9 1.4 Maximum Likelihood Framework 11 1.4.1 The Log-Likelihood Function 12 1.4.2 Gradient 17 1.4.3 Hessian 19 1.5 Applications 22 1.5.1 Stationary Distribution of the Vasicek Model 23 1.5.2 Transitional Distribution of the Vasicek Model 24 1.6 Exercises 27 2 Properties of Maximum Likelihood Estimators 33 2.1 Introduction 33 2.2 Preliminaries 33 2.2.1 Properties of Stochastic Time Series 34 2.2.2 Weak Law of Large Numbers 38 2.2.3 Rates of Convergence 42 2.2.4 Central Limit Theorems 45 2.3 Regularity Conditions 53 2.4 Properties of the Likelihood Function 54 2.4.1 The Population Likelihood Function 54 : Contents 2.4.2 Moments of the Gradient 55 2.4.3 The Information Matrix 58 2.5 Asymptotic Properties 60 2.5.1 Consistency 60 2.5.2 Normality 63 2.5.3 Efficiency 65 2.6 Finite-Sample Properties 68 2.6.1 Unbiasedness 69 2.6.2 Sufficiency 70 2.6.3 Invariance 71 2.6.4 Non-Uniqueness 72 2.7 Applications 72 2.7.1 Portfolio Diversification 74 2.7.2 Bimodal Likelihood 76 2.8 Exercises 78 3 Numerical Estimation Methods 87 3.1 Introduction 87 3.2 Newton Methods 88 3.2.1 Newton-Raphson 89 3.2.2 Method of Scoring 90 3.2.3 BHHH Algorithm 92 3.2.4 Comparative Examples 94 3.3 Quasi-Newton Methods 96 3.4 Line Searching 98 3.5 Optimisation Based on Function Evaluation 100 3.6 Computing Standard Errors 101 3.7 Hints for Practical Optimisation 104 3.7.1 Concentrating the Likelihood 104 3.7.2 Parameter Constraints 105 3.7.3 Choice of Algorithm 106 3.7.4 Numerical Derivatives 107 3.7.5 Starting Values 108 3.7.6 Convergence Criteria 108 3.8 Applications 109 3.8.1 Stationary Distribution of the CIR Model 109 3.8.2 Transitional Distribution of the CIR Model 111 3.9 Exercises 112 4 Hypothesis Testing 119 4.1 Introduction 119 4.2 Overview 119 4.3 Types of Hypotheses 121 Contents χι 4.3.1 Simple and Composite Hypotheses 121 4.3.2 Linear Hypotheses 122 4.3.3 Nonlinear Hypotheses 123 4.4 Likelihood Ratio Test 124 4.5 Wald Test 128 4.5.1 Linear Hypotheses 129 4.5.2 Nonlinear Hypotheses 130 4.6 Lagrange Multiplier Test 131 4.7 Distribution Theory 133 4.7.1 Asymptotic Distribution of the Wald Statistic 133 4.7.2 Asymptotic Relationships Among the Tests 136 4.7.3 Finite Sample Relationships 138 4.8 Size and Power Properties 139 4.8.1 Size of a Test 139 4.8.2 Power of a Test 140 4.9 Applications 141 4.9.1 Exponential Regression Model 142 4.9.2 Gamma Regression Model 144 4.10 Exercises 147 PART TWO Regression Models 5 Linear Regression Models 157 5.1 Introduction 157 5.2 Specification 158 5.2.1 Model Classification 158 5.2.2 Structural and Reduced Forms 160 5.3 Estimation 162 5.3.1 Single Equation: Ordinary Least Squares 162 5.3.2 Multiple Equations: FIML 167 5.3.3 Identification 171 5.3.4 Instrumental Variables 173 5.3.5 Seemingly Unrelated Regression (SUR) Models 176 5.4 Testing 177 5.5 Applications 183 5.5.1 Linear Taylor Rule 183 5.5.2 The Klein Model of the United States Economy 184 5.6 Exercises 187 6 Nonlinear Regression Models 194 6.1 Introduction 194 6.2 Specification 194 6.3 Maximum Likelihood Estimation 196 xii Contents 6.4 Gauss-Newton 202 6.4.1 Relationship to Nonlinear Least Squares 206 6.4.2 Relationship to Ordinary Least Squares 207 6.4.3 Asymptotic Distributions 207 6.5 Testing 208 6.5.1 LR, Wald and LM Tests 208 6.5.2 Non-nested Tests 212 6.6 Applications 215 6.6.1 Robust Estimation of the CAPM 215 6.6.2 Stochastic Frontier Models 218 6.7 Exercises 222 7 Autocorrelated Regression Models 228 7.1 Introduction 228 7.2 Specification 228 7.3 Maximum Likelihood Estimation 230 7.3.1 Exact Maximum Likelihood 230 7.3.2 Conditional Maximum Likelihood 232 7.4 Alternative Estimators 235 7.4.1 Gauss-Newton 235 7.4.2 Zig-Zag Algorithms 238 7.4.3 Cochrane-Orcutt 240 7.5 Distribution Theory 242 7.5.1 Maximum Likelihood Estimator 243 7.5.2 Least Squares Estimator 247 7.6 Lagged Dependent Variables 251 7.7 Testing 252 7.7.1 Alternative LM Test I 255 7.7.2 Alternative LM Test II 256 7.7.3 Alternative LM Test III 257 7.8 Autocorrelation in Systems of Equations 258 7.8.1 Estimation 259 7.8.2 Testing 260 7.9 Applications 261 7.9.1 Illiquidity and Hedge Funds 261 7.9.2 Beach-MacKinnon Simulation Study 262 7.10 Exercises 263 8 Heteroskedastic Regression Models 272 8.1 Introduction 272 8.2 Specification 272 8.3 Estimation 276 Contents хш 8.4 8.5 8.6 8.7 8.3.1 Maximum Likelihood 276 8.3.2 Relationship with Weighted Least Squares 279 Distribution Theory 281 Testing 282 Heteroskedasticity in Systems of Equations 288 8.6.1 Specification 288 8.6.2 Estimation 289 8.6.3 Testing 291 8.6.4 Heteroskedastic and Autocorrelated Disturbances 292 Applications 294 8.7.1 The Great Moderation 294 8.7.2 Finite Sample Properties of the Wald Test 295 Exercises 297 PART THREE Other Estimation Methods 10 Quasi-Maximum Likelihood Estimation 307 9.1 Introduction 307 9.2 Misspecification 308 9.3 The Quasi-Maximum Likelihood Estimator 312 9.4 Asymptotic Distribution 314 9.4.1 Misspecification and the Information Equality 317 9.4.2 Independent and Identically Distributed Data 320 9.4.3 Dependent Data and Martingale Difference Score 322 9.4.4 Dependent Data and Score 322 9.5 Variance Estimation 324 9.6 Quasi-Maximum Likelihood and Linear Regression 326 9.6.1 Misspecification: Non-normality 329 9.6.2 Misspecification: Heteroskedasticity 329 9.6.3 Misspecification: Autocorrelation 331 9.6.4 The White Variance Estimator 332 9.6.5 The Newey-West Variance Estimator 334 9.7 Testing 338 9.8 Applications 339 9.8.1 Autoregressive Models for Count Data 339 9.8.2 The CKLS Model of Interest Rates 342 9.9 Exercises 345 Generalised Method of Moments 352 10.1 Introduction 352 10.2 Motivating Examples 353 xiv Contents 10.2.1 Population Moments 353 10.2.2 Empirical Moments 354 10.2.3 GMM Models from Conditional Expectations 358 10.2.4 GMM and Maximum Likelihood 361 10.3 Estimation 362 10.3.1 The GMM Objective Function 362 10.3.2 Asymptotic Properties 364 10.3.3 Estimation Strategies 369 10.4 Over-identification Testing 373 10.5 Applications 378 10.5.1 Monte Carlo Evidence 378 10.5.2 Levels Effect in Interest Rates 381 10.6 Exercises 384 11 Nonparametric Estimation 392 11.1 Introduction 392 11.2 The Kernel Density Estimator 393 11.3 Properties of the Kernel Density Estimator 397 11.3.1 Finite Sample Properties 397 11.3.2 Optimal Bandwidth Selection 398 11.3.3 Asymptotic Properties 401 11.3.4 Dependent Data 403 11.4 Semi-parametric Density Estimation 404 11.5 The Nadaraya-Watson Kernel Regression Estimator 406 11.6 Properties of Kernel Regression Estimators 410 11.7 Bandwidth Selection for Kernel Regression 413 11.8 Multivariate Kernel Regression 416 11.9 Semi -parametric Regression of the Partial Linear Model 418 11.10 Applications 419 11.10.1 Derivatives of a Nonlinear Production Function 420 11.10.2 Drift and Diffusion Functions of SDEs 422 11.11 Exercises 424 12 Estimation by Simulation 432 12.1 Introduction 432 12.2 Motivating Example 433 12.3 Indirect Inference 435 12.3.1 Estimation 436 12.3.2 Relationship with Indirect Least Squares 439 12.4 Efficient Method of Moments (EMM) 441 12.4.1 Estimation 441 12.4.2 Relationship with Instrumental Variables 442 Contents xv 12.5 Simulated Generalised Method of Moments (SMM) 444 12.6 Estimating Continuous-Time Models 445 12.6.1 Brownian Motion 448 12.6.2 Geometric Brownian Motion 451 12.6.3 Stochastic Volatility 454 12.7 Applications 456 12.7.1 Finite Sample Properties 457 12.7.2 Empirical Properties 459 12.8 Exercises 460 PART FOUR Stationary Time Series 13 Linear Time Series Models 467 13.1 Introduction 467 13.2 Time Series Properties of Data 468 13.3 Specification 470 13.3.1 Univariate Model Classification 471 13.3.2 Multivariate Model Classification 474 13.4 Stationarity 476 13.4.1 The Stationarity Condition 476 13.4.2 Wold s Representation Theorem 477 13.4.3 Transforming a VAR to a VMA 478 13.5 Invertibility 481 13.5.1 The Invertibility Condition 481 13.5.2 Transforming a VMA to a VAR 481 13.6 Estimation 482 13.7 Optimal Choice of Lag Order 486 13.8 Distribution Theory 488 13.9 Testing 489 13.10 Analysing Vector Autoregressions 491 13.10.1 Granger Causality Testing 492 13.10.2 Impulse Response Functions 493 13.10.3 Variance Decompositions 498 13.11 Applications 500 13.11.1 Barro s Rational Expectations Model 501 13.11.2 The Campbell-Shiller Present Value Model 502 13.12 Exercises 504 14 Structural Vector Autoregressions 512 14.1 Introduction 512 14.2 Specification 513 14.2.1 Short-Run Restrictions 516 14.2.2 Long-Run Restrictions 519 xvi Contents 15 14.2.3 Short-Run and Long-Run Restrictions 522 14.2.4 Sign Restrictions 524 14.3 Estimation 527 14.4 Identification 531 14.5 Testing 533 14.6 Applications 535 14.6.1 Peersman s Model of Oil Price Shocks 535 14.6.2 A Portfolio SVAR Model of Australia 537 14.7 Exercises 539 Latent Factor Models 544 15.1 Introduction 544 15.2 Motivating Examples 545 15.2.1 Empirical 545 15.2.2 Theoretical 547 15.3 The Recursions of the Kalman Filter 548 15.3.1 Univariate 548 15.3.2 Multivariate 554 15.4 Extensions 557 15.4.1 Intercepts 557 15.4.2 Dynamics 557 15.4.3 Nonstationary Factors 558 15.4.4 Exogenous and Predetermined Variables 559 15.5 Factor Extraction 560 15.6 Estimation 561 15.6.1 Identification 561 15.6.2 Maximum Likelihood 562 15.6.3 Principal Components Estimator 564 15.7 Relationship to VARMA Models 566 15.8 Applications 567 15.8.1 The Hodrick-Prescott Filter 567 15.8.2 A Factor Model of Spreads with Money Shocks 571 15.9 Exercises 573 PART FIVE Nonstationary Time Series 16 Nonstationary Distribution Theory 583 16.1 Introduction 583 16.2 Specification . 584 16.2.1 Models of Trends 584 16.2.2 Integration 586 16.3 Estimation 588 16.3.1 Stationary Case 588 Contents xvii 16.3.2 Nonstationary Case: Stochastic Trends 591 16.3.3 Nonstationary Case: Deterministic Trends 594 16.4 Asymptotics for Integrated Processes 596 16.4.1 Brownian Motion 597 16.4.2 Functional Central Limit Theorem 598 16.4.3 Continuous Mapping Theorem 602 16.4.4 Stochastic Integrals 603 16.5 Multivariate Analysis 606 16.6 Applications 609 16.6.1 Least Squares Estimator of the AR(1) Model 609 16.6.2 Trend Estimation in the Presence of a Random Walk 611 16.7 Exercises 613 17 Unit Root Testing 619 17.1 Introduction 619 17.2 Specification 619 17.3 Detrending 621 17.3.1 Ordinary Least Squares (OLS) 623 17.3.2 First Differences 624 17.3.3 Generalised Least Squares (GLS) 625 17.4 Testing 626 17.4.1 Dickey-Fuller Tests 626 17.4.2 M Tests 627 17.5 Distribution Theory 629 17.5.1 Ordinary Least Squares Detrending 631 17.5.2 Generalised Least Squares Detrending 632 17.5.3 Simulating Critical Values 634 17.6 Power 636 17.6.1 Near Integration 637 17.6.2 Asymptotic Local Power 639 17.6.3 Point Optimal Tests 639 17.6:4 Asymptotic Power Envelope 641 17.7 Autocorrelation 642 17.7.1 Dickey-Fuller Test with Autocorrelation 642 17.7.2 M Tests with Autocorrelation 643 17.8 Structural Breaks 645 17.8.1 Known Break Point 647 17.8.2 Unknown Break Point 651 17.9 Applications 652 17.9.1 Power and the Initial Value 652 17.9.2 Nelson-Plosser Data Revisited 653 17.10 Exercises 655 xviii Contents 18 Comtegratíon 662 18.1 Introduction 662 18.2 Long-Run Economic Models 663 18.3 Specification of a VECM 665 18.3.1 Divariate Models 665 18.3.2 Multivariate Models 667 18.3.3 Cointegration 668 18.3.4 Deterministic Components 670 18.4 Estimation 672 18.4.1 Full-Rank Case 673 18.4.2 Reduced-Rank Case: Iterative Estimator 674 18.4.3 Reduced-Rank Case: Johansen Estimator 675 18.4.4 Zero-Rank Case 681 18.5 Identification 682 18.5.1 Triangular Restrictions 682 18.5.2 Structural Restrictions 683 18.6 Distribution Theory 684 18.6.1 Asymptotic Distribution of the Eigenvalues 684 18.6.2 Asymptotic Distribution of the Parameters 686 18.7 Testing 689 18.7.1 Cointegrating Rank 690 18.7.2 Cointegrating Vector 693 18.7.3 Exogeneity 695 18.8 Dynamics 696 18.8.1 Impulse Responses 696 18.8.2 Cointegrating Vector Interpretation 697 18.9 Applications 698 18.9.1 Rank Selection Based on Information Criteria 698 18.9.2 Effects of Heteroskedasticity on the Trace Test 700 18.10 Exercises 701 PART SIX Nonlinear Time Series 19 Nonlinearitìes in Mean 715 19.1 Introduction 715 19.2 Motivating Examples 715 19.3 Threshold Models 720 19.3.1 Specification 720 19.3.2 Estimation 722 19.3.3 Testing 723 19.4 Artificial Neural Networks 726 19.4.1 Specification 726 19.4.2 Estimation 728 Contents xix 19.4.3 Testing 731 19.5 Bilinear Time Series Models 732 19.5.1 Specification 732 19.5.2 Estimation 733 19.5.3 Testing 734 19.6 Markov Switching Model 734 19.7 Nonparametric Autoregression 738 19.8 Nonlinear Impulse Responses 740 19.9 Applications 744 19.9.1 A Multiple Equilibrium Model of Unemployment 744 19.9.2 Divariate Threshold Models of G7 Countries 745 19.10 Exercises 748 20 Nonlinearities in Variance 758 20.1 Introduction 758 20.2 Statistical Properties of Asset Returns 758 20.3 The ARCH Model 762 20.3.1 Specification 762 20.3.2 Estimation 763 20.3.3 Testing 767 20.4 Univariate Extensions 769 20.4.1 GARCH 769 20.4.2 Integrated GARCH 774 20.4.3 Additional Variables 775 20.4.4 Asymmetries 776 20.4.5 Garch-in-Mean 777 20.4.6 Diagnostics 779 20.5 Conditional Non-normality 780 20.5.1 Parametric 780 20.5.2 Semi-parametric 782 20.5.3 Nonparametric 783 20.6 Multivariate GARCH 786 20.6.1 VECH 787 20.6.2 BEKK 788 20.6.3 DCC 791 20.6.4 DECO 797 20.7 Applications 798 20.7.1 DCC and DECO Models of United States Yields 798 20.7.2 A Time-Varying Volatility SVAR Model 799 20.8 Exercises 802 21 Discrete Time Series Models 812 21.1 Introduction 812 xx Contents 21.2 Motivating Examples 812 21.3 Qualitative Data 815 21.3.1 Specification 815 21.3.2 Estimation 819 21.3.3 Testing 822 21.3.4 Binary Autoregressive Models 824 21.4 Ordered Data 826 21.5 Count Data 828 21.5.1 The Poisson Regression Model 830 21.5.2 Integer Autoregressive Models 831 21.6 Duration Data 835 21.7 Applications 837 21.7.1 An ACH Model of United States Airline Trades 837 21.7.2 EMM Estimator of Integer Models 840 21.8 Exercises 842 Appendix A: Change of Variable in Density Functions 849 Appendix B: The Lag Operator 850 B.I Basics 850 B.2 Polynomial Convolution 850 B.3 Polynomial Inversion 851 B.4 Polynomial Decomposition 852 Appendix C: FIML Estimation of a Structural Model 854 С 1 Log-Likelihood Function 854 C.2 First-Order Conditions 854 C.3 Solution 855 Appendix D: Additional Nonparametric Results 857 D.I Mean 857 D.2 Variance 859 D.3 Mean Square Error 861 D.4 Roughness 862 D.4. 1 Roughness Results for the Gaussian Distribution 862 D.4.2 Roughness Results for the Gaussian Kernel 863 References 865 Author Index 877 Subject Index 881 This textbook strikes an excellent balance between explaining the underlying concepts and intuition, containing the requisite amount of rigor, and providing sufficient guidance for students to be able to apply the methods described to a variety of time series situations. It is extremely clearly written and should instantly find a wide audience. The book s emphasis on maximum likelihood as a unifying guiding principle is well justified and provides the right context for students to understand how seemingly disparate econometric methods are fundamentally related. This book takes an important step forward relative to existing time-series econometrics texts.... In addition, readers will benefit immensely from the complete sets of included R and MATLAB routines. Well done! This book will be an excellent text for advanced undergraduate and postgraduate courses in econometric time series. The statistical theory is clearly presented and the many examples make the techniques readily accessible and illustrate their practical importance. This book is exceptionally well done. The blending of theory, application and computation is sublimely done throughout. This book will be a must-have for ... graduate students working with economic and financial time series data, and will also form a definitive and up-to-date referenco source for both academic and academic-related researchers in the field.
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id	DE-604.BV040363836
illustrated	Illustrated
indexdate	2024-12-20T16:13:28Z
institution	BVB
isbn	9780521196604 0521196604 9780521139816 0521139813
language	English
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-025217621
oclc_num	830810941
open_access_boolean
owner	DE-11 DE-91 DE-BY-TUM DE-945 DE-739 DE-N2 DE-19 DE-BY-UBM DE-355 DE-BY-UBR DE-M382 DE-521 DE-188 DE-Aug4 DE-473 DE-BY-UBG DE-523
owner_facet	DE-11 DE-91 DE-BY-TUM DE-945 DE-739 DE-N2 DE-19 DE-BY-UBM DE-355 DE-BY-UBR DE-M382 DE-521 DE-188 DE-Aug4 DE-473 DE-BY-UBG DE-523
physical	XXXV, 887 S. graph. Darst.
publishDate	2013
publishDateSearch	2013
publishDateSort	2013
publisher	Cambridge Univ. Press
record_format	marc
series2	Themes in modern econometrics
spellingShingle	Martin, Vance 1955- Hurn, Stan 1961- Harris, David 1969- Econometric modelling with time series specification, estimation and testing Zeitreihenanalyse (DE-588)4067486-1 gnd Ökonometrisches Modell (DE-588)4043212-9 gnd
subject_GND	(DE-588)4067486-1 (DE-588)4043212-9
title	Econometric modelling with time series specification, estimation and testing
title_auth	Econometric modelling with time series specification, estimation and testing
title_exact_search	Econometric modelling with time series specification, estimation and testing
title_full	Econometric modelling with time series specification, estimation and testing Vance Martin ; Stan Hurn ; David Harris
title_fullStr	Econometric modelling with time series specification, estimation and testing Vance Martin ; Stan Hurn ; David Harris
title_full_unstemmed	Econometric modelling with time series specification, estimation and testing Vance Martin ; Stan Hurn ; David Harris
title_short	Econometric modelling with time series
title_sort	econometric modelling with time series specification estimation and testing
title_sub	specification, estimation and testing
topic	Zeitreihenanalyse (DE-588)4067486-1 gnd Ökonometrisches Modell (DE-588)4043212-9 gnd
topic_facet	Zeitreihenanalyse Ökonometrisches Modell
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025217621&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025217621&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT martinvance econometricmodellingwithtimeseriesspecificationestimationandtesting AT hurnstan econometricmodellingwithtimeseriesspecificationestimationandtesting AT harrisdavid econometricmodellingwithtimeseriesspecificationestimationandtesting

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