Econometric modelling with time series: specification, estimation and testing
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| Main Authors: | , , |
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| Format: | Book |
| Language: | English |
| Published: |
New York, NY
Cambridge Univ. Press
2013
|
| Edition: | 1. publ. |
| Series: | Themes in modern econometrics
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis Klappentext |
| Item Description: | Incl. bibliogr. references and index |
| Physical Description: | XXXV, 887 S. graph. Darst. |
| ISBN: | 9780521196604 0521196604 9780521139816 0521139813 |
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| 100 | 1 | |a Martin, Vance |d 1955- |e Verfasser |0 (DE-588)170104249 |4 aut | |
| 245 | 1 | 0 | |a Econometric modelling with time series |b specification, estimation and testing |c Vance Martin ; Stan Hurn ; David Harris |
| 250 | |a 1. publ. | ||
| 264 | 1 | |a New York, NY |b Cambridge Univ. Press |c 2013 | |
| 300 | |a XXXV, 887 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Themes in modern econometrics | |
| 500 | |a Incl. bibliogr. references and index | ||
| 650 | 0 | 7 | |a Zeitreihenanalyse |0 (DE-588)4067486-1 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Ökonometrisches Modell |0 (DE-588)4043212-9 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Zeitreihenanalyse |0 (DE-588)4067486-1 |D s |
| 689 | 0 | 1 | |a Ökonometrisches Modell |0 (DE-588)4043212-9 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Hurn, Stan |d 1961- |e Verfasser |0 (DE-588)171054784 |4 aut | |
| 700 | 1 | |a Harris, David |d 1969- |e Verfasser |0 (DE-588)171212347 |4 aut | |
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| 856 | 4 | 2 | |m Digitalisierung UB Passau |q application/pdf |u 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 |3 Klappentext |
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| _version_ | 1804149408849199104 |
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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.
|
| any_adam_object | 1 |
| author | Martin, Vance 1955- Hurn, Stan 1961- Harris, David 1969- |
| author_GND | (DE-588)170104249 (DE-588)171054784 (DE-588)171212347 |
| author_facet | Martin, Vance 1955- Hurn, Stan 1961- Harris, David 1969- |
| author_role | aut aut aut |
| author_sort | Martin, Vance 1955- |
| author_variant | v m vm s h sh d h dh |
| building | Verbundindex |
| bvnumber | BV040363836 |
| classification_rvk | QH 237 QH 330 |
| classification_tum | WIR 017f |
| ctrlnum | (OCoLC)830810941 (DE-599)BSZ368433854 |
| dewey-full | 330.01/51955 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 330 - Economics |
| dewey-raw | 330.01/51955 |
| dewey-search | 330.01/51955 |
| dewey-sort | 3330.01 551955 |
| dewey-tens | 330 - Economics |
| discipline | Wirtschaftswissenschaften |
| edition | 1. publ. |
| format | Book |
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| illustrated | Illustrated |
| indexdate | 2024-07-10T00:22:31Z |
| institution | BVB |
| isbn | 9780521196604 0521196604 9780521139816 0521139813 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-025217621 |
| oclc_num | 830810941 |
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| physical | XXXV, 887 S. graph. Darst. |
| publishDate | 2013 |
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| series2 | Themes in modern econometrics |
| spelling | Martin, Vance 1955- Verfasser (DE-588)170104249 aut Econometric modelling with time series specification, estimation and testing Vance Martin ; Stan Hurn ; David Harris 1. publ. New York, NY Cambridge Univ. Press 2013 XXXV, 887 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Themes in modern econometrics Incl. bibliogr. references and index Zeitreihenanalyse (DE-588)4067486-1 gnd rswk-swf Ökonometrisches Modell (DE-588)4043212-9 gnd rswk-swf Zeitreihenanalyse (DE-588)4067486-1 s Ökonometrisches Modell (DE-588)4043212-9 s DE-604 Hurn, Stan 1961- Verfasser (DE-588)171054784 aut Harris, David 1969- Verfasser (DE-588)171212347 aut Digitalisierung UB Passau application/pdf 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 Inhaltsverzeichnis Digitalisierung UB Passau application/pdf 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 Klappentext |
| 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 |
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