Structural vector autoregressive analysis:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2017
|
| Schriftenreihe: | Themes in modern econometrics
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Hier auch später erschienene, unveränderte Nachdrucke |
| Beschreibung: | xx, 734 Seiten Diagramme |
| ISBN: | 9781316647332 9781107196575 |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV044534461 | ||
| 003 | DE-604 | ||
| 005 | 20210211 | ||
| 007 | t | ||
| 008 | 171012s2017 |||| |||| 00||| eng d | ||
| 020 | |a 9781316647332 |c pbk. : £34.99 |9 978-1-316-64733-2 | ||
| 020 | |a 9781107196575 |c hbk. : £125.00 |9 978-1-107-19657-5 | ||
| 035 | |a (OCoLC)1018365050 | ||
| 035 | |a (DE-599)BSZ492882177 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-11 |a DE-N2 |a DE-355 |a DE-188 |a DE-473 |a DE-20 |a DE-706 |a DE-522 | ||
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| 084 | |a QH 234 |0 (DE-625)141549: |2 rvk | ||
| 100 | 1 | |a Kilian, Lutz |e Verfasser |0 (DE-588)130444812 |4 aut | |
| 245 | 1 | 0 | |a Structural vector autoregressive analysis |c Lutz Kilian (University of Michigan), Helmut Lütkepohl (DIW and Freie Universität Berlin) |
| 264 | 1 | |a Cambridge |b Cambridge University Press |c 2017 | |
| 300 | |a xx, 734 Seiten |b Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Themes in modern econometrics | |
| 500 | |a Hier auch später erschienene, unveränderte Nachdrucke | ||
| 650 | 4 | |a Ökonometrisches Modell | |
| 650 | 0 | 7 | |a Strukturelles vektor-autoregressives Modell |0 (DE-588)4288535-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Makroökonomie |0 (DE-588)4037174-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Regressionsanalyse |0 (DE-588)4129903-6 |2 gnd |9 rswk-swf |
| 653 | 0 | |a Econometric models | |
| 653 | 0 | |a Autoregression (Statistics) | |
| 653 | 0 | |a Regression analysis | |
| 653 | 0 | |a Monetary policy / Econometric models | |
| 655 | 7 | |0 (DE-588)4123623-3 |a Lehrbuch |2 gnd-content | |
| 689 | 0 | 0 | |a Strukturelles vektor-autoregressives Modell |0 (DE-588)4288535-8 |D s |
| 689 | 0 | 1 | |a Regressionsanalyse |0 (DE-588)4129903-6 |D s |
| 689 | 0 | 2 | |a Makroökonomie |0 (DE-588)4037174-8 |D s |
| 689 | 0 | |C b |5 DE-604 | |
| 700 | 1 | |a Lütkepohl, Helmut |d 1951- |e Verfasser |0 (DE-588)10979544X |4 aut | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-1-108-16481-8 |
| 856 | 4 | 2 | |m Digitalisierung UB Bamberg - ADAM Catalogue Enrichment |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029933663&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-029933663 | ||
Datensatz im Suchindex
| _version_ | 1804177888605372416 |
|---|---|
| adam_text | Contents
Preface page xvii
1 Introduction 1
1.1 Overview 1
1.2 Outline of the Book 4
2 Vector Autoregressive Models 19
2.1 Stationary and Trending Processes 19
2.2 Linear VAR Processes 23
2.2.1 The Basic Model 23
2.2.2 The Moving Average Representation 26
2.2.3 VAR Models as an Approximation to VARMA
Processes 27
2.2.4 Marginal Processes, Measurement Errors,
Aggregation, Variable Transformations 28
2.3 Estimation of VAR Models 30
2.3.1 Least-Squares Estimation 31
2.3.2 Restricted Generalized Least Squares 34
2.3.3 Bias-Corrected LS 35
2.3.4 Maximum Likelihood Estimation 38
2.3.5 VAR Processes in Levels with Integrated Variables 41
2.3.6 Sieve Autoregressions 43
2.4 Prediction 46
2.4.1 Predicting from Known VAR Processes 46
2.4.2 Predicting from Estimated VAR Processes 47
2.5 Granger Causality Analysis 48
2.6 Lag-Order Selection Procedures 51
2.6.1 Top-Down Sequential Testing 51
2.6.2 Bottom-Up Sequential Testing 52
2.6.3 Information Criteria 54
v
vi Contents
2.6.4 Recursive Mean-Squared Prediction Error Rankings 57
2.6.5 The Relative Merits of Alternative Lag-Order
Selection Tools 58
2.7 Model Diagnostics 66
2.7.1 Tests for Autocorrelation in the innovations 67
2.7.2 Tests for Nonnormality 67
2.7.3 Residual ARCH Tests 68
2.7.4 Time Invariance 69
2.8 Subset VAR Models, AVAR Models, and VARX Models 72
2.8.1 Subset VAR Models 72
2.8.2 Asymmetric VAR Models 73
2.8.3 VARX Models 74
3 Vector Error Correction Models 75
3.1 Cointegrated Variables and Vector Error Correction Models 75
3.1.1 Common Trends and Cointegration 75
3.1.2 Deterministic Terms in Cointegrated Processes 80
3.2 Estimation of VARs with Integrated Variables 82
3.2.1 The VAR(l) Case 82
3.2.2 Estimation of VECMs 86
3.2.3 Estimation of Levels VAR Models with Integrated
Variables 95
3.3 Model Specification 99
3.3.1 Choosing the Lag Order 99
3.3.2 Specifying the Cointegrating Rank 100
3.4 Diagnostic Tests 104
3.5 The Benefits of the VECM Representation 105
3.6 Practical Issues 105
3.6.1 Limitations of Tests for Unit Roots and Cointegration 106
3.6.2 Alternative Approaches 106
4 Structural VAR Tools 109
4.1 Structural Impulse Responses 110
4.2 Forecast Error Variance Decompositions 113
4.3 Historical Decompositions 116
4.4 Forecast Scenarios 123
4.4.1 Conditional Forecasts Expressed in Terms of
Sequences of Structural Shocks 124
4.4.2 Conditional Forecasts Expressed in Terms of
Sequences of Observables 130
4.5 Simulating Counterfactual Outcomes 131
4.6 Policy Counterfactuals 136
Contents vii
5 Bayesian VAR Analysis 140
5.1 Basic Terms and Notation 141
5.1.1 Prior, Likelihood, Posterior 141
5.1.2 Bayesian Estimation and Inference 142
5.1.3 Simulating the Posterior Distribution 145
5.2 Priors for Reduced-Form VAR Parameters 149
5.2.1 General Procedures for Choosing the Parameters of
Prior Densities 150
5.2.2 Normal Prior for the VAR Parameters for Given 151
5.2.3 The Original Minnesota Prior 155
5.2.4 The Natural Conjugate Gaussian-Inverse Wishart
Prior 162
5.2.5 The Independent Gaussian-Inverse Wishart Prior 166
5.3 Extensions and Related Issues 169
6 The Relationship between VAR Models and Other
Macroeconometric Models 171
6.1 The Relationship between VAR Models and Traditional
Dynamic Simultaneous Equations Models 171
6.1.1 The VAR Representation of Traditional DSEMs 172
6.1.2 Incredible Restrictions in Traditional DSEMs 174
6.1.3 Structural VAR Models as an Alternative to
Traditional DSEMs 176
6.2 The Relationship between VAR Models and DSGE Models 177
6.2.1 Basics 177
6.2.2 The Role of Data Transformations 180
6.2.3 Why Not Use VARMA Models? 180
6.2.4 Autoregressive Sieve Approximations of VAR(oo)
Processes 181
6.2.5 Summary of Potential Problems in Approximating
DSGE Models with VAR Models 182
6.3 DSGE Models as an Alternative to VAR Models? 183
6.3.1 Calibrated DSGE Models 184
6.3.2 Estimated DSGE Models 185
6.3.3 Calibration versus Bayesian Estimation 186
6.3.4 Are Structural VAR Models Less Credible than
DSGE Models? 187
6.3.5 Are DSGE Models More Accurate than VAR
Models? 189
6.3.6 Policy Analysis in DSGE Models and SVAR Models 191
6.4 An Overview of Alternative Structural Macroeconometric
Models 193
Contents
viii
6.4.1 Combining DSEMs and SVAR Models 193
6.4.2 Combining DSGE and SVAR Models 194
7 A Historical Perspective on Causal Inference in
Macroeconometrics 196
7.1 A Motivating Example 196
7.2 Granger Causality Tests for Covariance Stationary VAR
Models 197
7.3 Granger Causality, Predeterminedness, and Exogeneity 199
7.3.1 Basic Concepts 199
7.3.2 Granger Causality and Forward-Looking Behavior 201
7.3.3 Strict Exogeneity in Modem Macroeconomic Models 203
7.4 The Demise of Granger Causality Tests in Macroeconomics 204
7.5 Responses to Unanticipated Changes in Money Growth 205
7.5.1 The Narrative Approach 205
7.5.2 Exogenous Shocks Derived from Data-Based
Counterfactuals 208
7.5.3 News Shocks 209
7.5.4 Shocks to Financial Market Expectations 210
7.5.5 Summary 211
7.6 Structural VAR Shocks 211
7.6.1 The Identification Problem 212
7.6.2 The Relationship between Structural VAR Shocks
and Direct Shock Measures 213
7.6.3 Causality in Structural VAR Models 214
8 Identification by Short-Run Restrictions 216
8.1 introduction 216
8.2 Recursively Identified Models 219
8.3 Sources of Identifying Restrictions 221
8.4 Examples of Recursively Identified Models 224
8.4.1 A Simple Macroeconomic Model 224
8.4.2 A Model of the Global Market for Crude Oil 225
8.4.3 Oil Price Shocks and Stock Returns 226
8.4.4 Models of the Transmission of Energy Price Shocks 227
8.4.5 Semistructural Models of Monetary Policy 228
8.4.6 The Permanent Income Model of Consumption 234
8.5 Examples of Nonrecursively Identified Models 235
8.5.1 Fiscal Policy Shocks 236
8.5.2 An Alternative Simple Macroeconomic Model 237
8.5.3 Discussion 237
8.5.4 The Graph-Theoretic Approach 238
8.6 Summary 239
Contents ix
9 Estimation Subject to Short-Run Restrictions 241
9.1 Model Setup 241
9.2 Method-of-Moments Estimation 242
9.2.1 Recursively Identified Models 242
9.2.2 Nonrecursively Identified Models 250
9.2.3 GMM Estimation of Overidentified Models 253
9.3 Instrumental Variable Estimation 258
9.4 Full Information Maximum Likelihood Estimation 262
9.5 Bayesian Estimation 265
9.6 Summary 268
10 Identification by Long-Run Restrictions 269
10.1 The Traditional Framework for Imposing Long-Run
Restrictions 269
10.2 A General Framework for Imposing Long-Run
Restrictions 272
10.2.1 The Long-Run Multiplier Matrix 272
10.2.2 Identification of Structural Shocks 275
10.3 Examples of Long-Run Restrictions 278
10.3.1 A Real Business Cycle Model with and without
Nominal Variables 278
10.3.2 A Model of Neutral and Investment-Specific
Technology Shocks 282
10.3.3 A Model of Real and Nominal Exchange Rate
Shocks 284
10.3.4 A Model of Expectations about Future Productivity 284
10.4 Examples of Models Combining Long-Run and Short-Run
Zero Restrictions 287
10.4.1 The 1S-LM Model Revisited 287
10.4.2 A Model of the Neoclassical Synthesis 289
10.4.3 A U.S. Macroeconomic Model 290
10.5 Limitations of Long-Run Restrictions 292
10.5.1 Long-Run Restrictions Require Exact Unit Roots 292
10.5.2 Sensitivity to Omitted Variables 293
10.5.3 Lack of Robustness at Lower Data Frequencies 294
10.5.4 Nonuniqueness Problems without Additional Sign
Restrictions 294
10.5.5 Sensitivity to Data Transformations 296
11 Estimation Subject to Long-Run Restrictions 297
11.1 Model Setup 297
11.2 Models Subject to Long-Run Restrictions Only 299
11.2.1 Method-of-Moments Estimation 301
X
Contents
11.2.2 Full Information Maximum Likelihood Estimation 306
11.2.3 Instrumental Variable Estimation 307
11.3 Models Subject to Long-Run and Short-Run Restrictons 310
11.3.1 Estimating the Model in VAR Representation 310
11.3.2 Estimating the Model in VECM Representation 316
11.4 Practical Limitations of Long-Run Restrictions 320
11.4.1 Estimators of the Long-Run Multiplier Matrix May
Be Unreliable 321
l L4.2 Lack of Power 321
11.4.3 Near-Observational Equivalence of Shocks with
Permanent Effects and Shocks with Persistent
Effects 322
11.4.4 Weak Instrument Problems 322
11.5 Can Structural VAR Models Recover Responses in
DSGE Models? 323
11.5.1 The Origin of This Controversy 323
11.5.2 The Position of Chari et al. (2008) 325
11.5.3 The Position of Christiano et al. (2006) 327
11.5.4 Understanding the Simulation Evidence 328
11.5.5 Summary 331
12 Inference in Models Identified by Short-Run or
Long-Run Restrictions 334
12.1 Delta Method Intervals for Structural Impulse Responses 335
12.1.1 Finite-Order VAR Models 336
12.1.2 Infinite-Order VAR Models 338
12.1.3 Discussion 339
12.1.4 Extensions to Other Statistics 339
12.1.5 On the Choice of the Significance Level 340
12.2 Bootstrap Intervals for Structural Impulse Responses 340
12.2.1 The Standard Residual-Based Recursive-Design
Bootstrap 341
12.2.2 The Standard Residual-Based Fixed-Design
Bootstrap 345
12.2.3 The Residual-Based Wild Bootstrap 345
12.2.4 Bootstrapping Tuples of Regressands and
Regressors 347
12.2.5 Block Bootstrap Methods 348
12.2.6 Alternative Bootstrap Confidence Intervals 356
12.3 Bootstrap Intervals Based on Bias-Adjusted Estimators 363
12.4 Potential Pitfalls in Impulse Response Inference 365
12.5 Finite-Sample Properties of Bootstrap Confidence
Intervals 368
Contents
xi
12.6 Inference for Integrated and Cointegrated VAR Processes 369
12.6.1 VAR Models in Differences 369
12.6.2 Vector Error Correction Models 370
12.6.3 Integrated and/or Cointegrated VAR Models in
Levels 373
12.7 Inference in Local-to-Unity VAR Processes 377
12.7.1 Local-to-Unity Asymptotics 378
12.7.2 Inference in Levels for Local-to-Unity VAR
Models 381
12.7.3 The Grid Bootstrap Method 382
12.7.4 A Hybrid Method 384
12.7.5 Implications for Second-Stage Inference after
Pretesting 385
12.8 Local Projections 389
12.9 Synthesis 393
12.10 Bayesian Regions of Highest Posterior Density 394
12.10.1 Pointwise Inference on Structural Impulse
Responses 395
12.11 Joint Inference on Structural Impulse Responses 398
12.11.1 Joint Confidence Sets for Structural Impulse
Responses 399
12.11.2 Joint Credible Sets 406
12.12 Other Bootstrap Applications 410
12.12.1 Bootstrap Prediction 410
12.12.2 Bootstrapping the Critical Values of Test
Statistics 411
12.13 Examples of Impulse Response Confidence Intervals 412
12.13.1 An Exactly Identified Model 412
12.13.2 Guarding against Conditional
Heteroskedasticity 415
12.13.3 Extensions to Overidentified Models 416
13 Identification by Sign Restrictions 421
13.1 A Model of Demand and Supply 421
13.2 How to Impose Static Sign Restrictions 424
13.2.1 Givens Rotation Matrices 426
13.2.2 The Householder Transformation 427
13.2.3 The Ouliaris-Pagan Approach 428
13.3 Partially Identified VAR Models 430
13.4 Beyond Static Sign Restrictions 432
xii Contents
13.4.1 Dynamic Sign Restrictions 432
13.4.2 Elasticity Bounds 432
13.4.3 Shape Restrictions 435
13.5 Can Sign Restrictions Be Verified? 435
13.6 Estimation and Inference in Sign-Identified
VAR Models 437
13.6.1 Frequentist Approaches 438
13.6.2 Bayesian Approaches 440
13.6.3 Evaluating the Posterior of the Structural
Impulse Responses 442
13.6.4 The Penalty Function Approach 448
13.6.5 Using Historical Information to Narrow the Set
of Admissible Models 451
13.7 The Role of the Prior for the Rotation Matrix 452
13.7.1 An Approach Based on Explicit Bayesian Priors
for Bo 453
13.7.2 An Approach Based on Explicit Bayesian Priors
for the Structural Impulse Responses 459
13.7.3 A Robust Bayesian Approach 461
13.7.4 An Agnostic Bayesian Approach 462
13.7.5 A Non-Bayesian Approach 463
13.8 Examples of Models Identified by Sign Restrictions 464
13.8.1 A Small-Scale Macroeconomic Model 464
13.8.2 A Slightly Larger Macroeconomic Model 465
13.8.3 A Model of Unemployment and Vacancies 466
13.8.4 An Extended Model of Unemployment and
Vacancies 466
13.8.5 A Model of Technology Shocks 467
13.8.6 A Model of Exchange Rate Responses to
Monetary Policy Shocks 467
13.8.7 A Medium-Scale Macroeconomic Model 468
13.8.8 A Model of Speculation in the Global Oil
Market 469
13.9 Mixing Sign and Exclusion Restrictions 471
13.9.1 Examples of Models Mixing Sign and
Short-Run Zero Restrictions 471
13.9.2 How to Combine Sign Restrictions and
Exclusion Restrictions 474
13.9.3 Discussion 482
13.10 Empirical Illustrations 483
13.10.1 A Model of the Global Oil Market 483
13.10.2 A Model of Monetary Policy 485
13.11 Concluding Remarks 488
xiii
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577
Contents
Identification by Heteroskedasticity or Non-Gaussianity
14.1 Introduction
14.2 The Model Setup
14.2.1 The Baseline Model
14.2.2 An Illustrative Example
14.2.3 The General Model
14.3 Alternative Volatility Models
14.3.1 Structural VAR Models with Extraneously
Specified Volatility Changes
14.3.2 Structural VAR Models with Markov Switching
in the Variances
14.3.3 Structural VAR Models with Smooth
Transitions in the Variances
14.3.4 Structural VAR Models with GARCH Errors
14.4 Alternative Approaches Using Heteroskedasticity
14.4.1 Time-Varying Instantaneous Effects
14.4.2 Correlated Shocks
14.5 Identification by Non-Gaussianity
14.5.1 Independent Shocks
14.5.2 Uncorrelated Shocks
14.6 Discussion
Identification Based on Extraneous Data
15.1 Identification Based on High-Frequency Futures Prices
15.1.1 A Set- Identified Approach
15.1.2 A Point-Identified Approach
15.1.3 Discussion
15.2 Identification Based on External Instruments
15.2.1 Estimation and Inference
15.2.2 Discussion
Structural VAR Analysis in a Data-Rich Environment
16.1 Factor Models
16.1.1 Stati c Factor Model s
16.1.2 Dynamic Factor Models
16.1.3 Selecting the Number of Factors
16.1.4 Structural Change
16.2 Factor-Augmented Structural VAR Models and Related
Techniques
16.2.1 Structural FAVAR Models
16.2.2 Structural Analysis with DFMs
16.2.3 Empirical Examples of FAVAR Models and
DFMs
XIV
Contents
16.3 Large Bayesian VAR Models 579
16.3.1 Priors for Large Bayesian VARs 580
16.3.2 Structural Identification in Large BVARs 583
16.4 Alternative Large-Dimensional VAR Models 584
16.4.1 Panel VARs 584
16.4.2 Global VARs 586
16.4.3 Spatial Models 587
16.5 Discussion 587
17 Nonfundamental Shocks 590
17.1 Introduction 590
17.2 Fundamental and Nonfundamental Moving
Average Representations 592
17.3 Fundamental versus Nonfundamental Representations 594
17.3.1 Nonfundamental Shocks in Economic Models 594
17.3.2 Nonfundamentalness Due to MA Roots in the
Unit Circle 596
17.3.3 Nonfundamentalness Due to Omitted Variables 597
17.3.4 Avoiding Nonfundamentalness by Using
Factor-Augmented or Large Bayesian VARs 601
17.3.5 Other Approaches to Dealing with Anticipation 603
17.4 Conclusions 607
18 Nonlinear Structural VAR Models 609
18.1 Motivation 609
18.2 Nonlinear VAR Analysis 612
18.2.1 General Setup 612
18.2.2 Structural Analysis 614
18.3 Threshold and Smooth-Transition VAR Models 619
18.3.1 Model Setup 619
18.3.2 Example: A TVAR Model of U.S. Monetary
Policy 621
18.4 Markov-Switching VAR Models 622
18.4.1 Model Setup 623
18.4.2 Identification 625
18.4.3 Estimation 626
18.4.4 Model Selection 628
18.4.5 Example: An MS-VAR Model of U.S. Monetary
Policy 629
18.5 Time-Varying Coefficient VAR Models 630
18.5.1 Model Setup 631
18.5.2 Estimation 633
Contents xv
18.5.3 Example: A TVC-VAR Model of U.S. Monetary
Policy 635
18.6 VAR Models with GARCH-in-Mean 636
18.6.1 Model Setup 636
18.6.2 Estimation 637
18.6.3 Example: The Effect of Oil Price Uncertainty on
U.S. Real Output 638
18.7 Other Nonlinear Models 640
18.7.1 Nonparametric VAR Analysis 640
18.7.2 Noncausal VAR Models 645
18.8 Discussion of Nonlinear VAR Modeling 648
18.9 Linear Structural Models with Nonlinear
Transformations of the Variables 650
18.9.1 The Censored Oil Price VAR Model 651
18.9.2 A Nonlinear Structural Model Allowing for
Asymmetric Responses 652
18.9.3 Quantifying Nonlinear Responses to Oil Price
Shocks 653
18.9.4 Testing the Null of Unconditionally Symmetric
Response Functions 654
18.9.5 Testing the Null of Conditionally Symmetric
Response Functions 655
18.9.6 Testing the Null of No Time Dependence 656
18.9.7 Conditional Prediction Error Decompositions 657
18.9.8 Extensions 658
19 Practical Issues Related to Trends, Seasonality, and
Structural Change 659
19.1 Alternative Trend Models 659
19.1.1 Hodrick-Prescott (HP) Filter 659
19.1.2 Band-Pass Filters 660
19.1.3 Potential Shortcomings of Trend Filters 661
19.1.4 Trend-Filtered Variables in VAR Models 661
19.1.5 Choosing between Different Trend Models 662
19.1.6 Combining Different Trend Specifications 662
19.2 Seasonality 663
19.2.1 Deterministic Seasonal Variation in VAR
Models 663
19.2.2 Stochastic Seasonal Variation in VAR Models 664
19.2.3 Synthesis 666
19.2.4 Periodic Seasonal VAR Models 666
19.2.5 Seasonal TVC-VAR Models 667
XVI
Contents
19.2.6 Seasonally Filtered Data in VAR Models 667
19.2.7 Combining Seasonally Adjusted and Unadjusted
Data in the same VAR Model 668
19.2.8 Summary 668
19.3 Structural Change in the Stochastic Component of
the VAR Model 669
19.3.1 Breaks in the Stochastic Component 669
19.3.2 Smooth Structural Change in the Stochastic
Component 671
Bibliography 673
Notation and Abbreviations 713
Author Index 721
Subject Index 729
|
| any_adam_object | 1 |
| author | Kilian, Lutz Lütkepohl, Helmut 1951- |
| author_GND | (DE-588)130444812 (DE-588)10979544X |
| author_facet | Kilian, Lutz Lütkepohl, Helmut 1951- |
| author_role | aut aut |
| author_sort | Kilian, Lutz |
| author_variant | l k lk h l hl |
| building | Verbundindex |
| bvnumber | BV044534461 |
| classification_rvk | QH 237 QH 234 |
| ctrlnum | (OCoLC)1018365050 (DE-599)BSZ492882177 |
| dewey-full | 330.01519536 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 330 - Economics |
| dewey-raw | 330.01519536 |
| dewey-search | 330.01519536 |
| dewey-sort | 3330.01519536 |
| dewey-tens | 330 - Economics |
| discipline | Wirtschaftswissenschaften |
| format | Book |
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| genre | (DE-588)4123623-3 Lehrbuch gnd-content |
| genre_facet | Lehrbuch |
| id | DE-604.BV044534461 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:55:14Z |
| institution | BVB |
| isbn | 9781316647332 9781107196575 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029933663 |
| oclc_num | 1018365050 |
| open_access_boolean | |
| owner | DE-11 DE-N2 DE-355 DE-BY-UBR DE-188 DE-473 DE-BY-UBG DE-20 DE-706 DE-522 |
| owner_facet | DE-11 DE-N2 DE-355 DE-BY-UBR DE-188 DE-473 DE-BY-UBG DE-20 DE-706 DE-522 |
| physical | xx, 734 Seiten Diagramme |
| publishDate | 2017 |
| publishDateSearch | 2017 |
| publishDateSort | 2017 |
| publisher | Cambridge University Press |
| record_format | marc |
| series2 | Themes in modern econometrics |
| spelling | Kilian, Lutz Verfasser (DE-588)130444812 aut Structural vector autoregressive analysis Lutz Kilian (University of Michigan), Helmut Lütkepohl (DIW and Freie Universität Berlin) Cambridge Cambridge University Press 2017 xx, 734 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Themes in modern econometrics Hier auch später erschienene, unveränderte Nachdrucke Ökonometrisches Modell Strukturelles vektor-autoregressives Modell (DE-588)4288535-8 gnd rswk-swf Makroökonomie (DE-588)4037174-8 gnd rswk-swf Regressionsanalyse (DE-588)4129903-6 gnd rswk-swf Econometric models Autoregression (Statistics) Regression analysis Monetary policy / Econometric models (DE-588)4123623-3 Lehrbuch gnd-content Strukturelles vektor-autoregressives Modell (DE-588)4288535-8 s Regressionsanalyse (DE-588)4129903-6 s Makroökonomie (DE-588)4037174-8 s b DE-604 Lütkepohl, Helmut 1951- Verfasser (DE-588)10979544X aut Erscheint auch als Online-Ausgabe 978-1-108-16481-8 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029933663&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Kilian, Lutz Lütkepohl, Helmut 1951- Structural vector autoregressive analysis Ökonometrisches Modell Strukturelles vektor-autoregressives Modell (DE-588)4288535-8 gnd Makroökonomie (DE-588)4037174-8 gnd Regressionsanalyse (DE-588)4129903-6 gnd |
| subject_GND | (DE-588)4288535-8 (DE-588)4037174-8 (DE-588)4129903-6 (DE-588)4123623-3 |
| title | Structural vector autoregressive analysis |
| title_auth | Structural vector autoregressive analysis |
| title_exact_search | Structural vector autoregressive analysis |
| title_full | Structural vector autoregressive analysis Lutz Kilian (University of Michigan), Helmut Lütkepohl (DIW and Freie Universität Berlin) |
| title_fullStr | Structural vector autoregressive analysis Lutz Kilian (University of Michigan), Helmut Lütkepohl (DIW and Freie Universität Berlin) |
| title_full_unstemmed | Structural vector autoregressive analysis Lutz Kilian (University of Michigan), Helmut Lütkepohl (DIW and Freie Universität Berlin) |
| title_short | Structural vector autoregressive analysis |
| title_sort | structural vector autoregressive analysis |
| topic | Ökonometrisches Modell Strukturelles vektor-autoregressives Modell (DE-588)4288535-8 gnd Makroökonomie (DE-588)4037174-8 gnd Regressionsanalyse (DE-588)4129903-6 gnd |
| topic_facet | Ökonometrisches Modell Strukturelles vektor-autoregressives Modell Makroökonomie Regressionsanalyse Lehrbuch |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029933663&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT kilianlutz structuralvectorautoregressiveanalysis AT lutkepohlhelmut structuralvectorautoregressiveanalysis |