Applied time series econometrics:
Time series econometrics is a rapidly evolving field. In particular, the cointegration revolution has had a substantial impact on applied analysis. As a consequence of the fast pace of development there are no textbooks that cover the full range of methods in current use and explain how to proceed i...
Saved in:
| Format: | Book |
|---|---|
| Language: | English |
| Published: |
Cambridge [u.a.]
Cambridge Univ. Press
2007
|
| Edition: | 1. publ., transf. to digital print. |
| Series: | Themes in modern econometrics
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Summary: | Time series econometrics is a rapidly evolving field. In particular, the cointegration revolution has had a substantial impact on applied analysis. As a consequence of the fast pace of development there are no textbooks that cover the full range of methods in current use and explain how to proceed in applied domains. This gap in the literature motivates the present volume. The methods are sketched out briefly to remind the reader of the ideas underlying them and to give sufficient background for empirical work. The treatment can also be used as a textbook for a course on applied time series econometrics. The coverage of topics follows recent methodological developments. Unit root and cointegration analysis play a central part. Other topics include structural vector autoregressions, conditional heteroskedasticity and nonlinear and nonparametric time series models. A crucial component in empirical work is the software that is available for analysis. New methodologyis typically only gradually incorporated into the existing softwarepackages. Therefore a felxible Java interface has been created that allows readers to replicate the applications and conduct their own analyses. |
| Item Description: | Literaturverz. S. 301 - 315 |
| Physical Description: | XXV, 323 S. graph. Darst. |
| ISBN: | 052183919X 9780521839198 0521547873 9780521547871 |
Staff View
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| 490 | 0 | |a Themes in modern econometrics | |
| 500 | |a Literaturverz. S. 301 - 315 | ||
| 520 | 3 | |a Time series econometrics is a rapidly evolving field. In particular, the cointegration revolution has had a substantial impact on applied analysis. As a consequence of the fast pace of development there are no textbooks that cover the full range of methods in current use and explain how to proceed in applied domains. This gap in the literature motivates the present volume. The methods are sketched out briefly to remind the reader of the ideas underlying them and to give sufficient background for empirical work. The treatment can also be used as a textbook for a course on applied time series econometrics. The coverage of topics follows recent methodological developments. Unit root and cointegration analysis play a central part. Other topics include structural vector autoregressions, conditional heteroskedasticity and nonlinear and nonparametric time series models. A crucial component in empirical work is the software that is available for analysis. New methodologyis typically only gradually incorporated into the existing softwarepackages. Therefore a felxible Java interface has been created that allows readers to replicate the applications and conduct their own analyses. | |
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Record in the Search Index
| _version_ | 1804137772179521536 |
|---|---|
| adam_text | Contents
Preface
page
xv
Notation and Abbreviations
xix
List of Contributors
xxv
1
Initial Tasks and Overview
1
Helmut Liitkepohl
1.1
Introduction
1
1.2
Setting Up an Econometric Project
2
1.3
Getting Data
3
1.4
Data Handling
5
1.5
Outline of Chapters
5
2
Univariate Time Series Analysis
8
Helmut Liitkepohl
2.1
Characteristics of Time Series
8
2.2
Stationary and Integrated Stochastic Processes
11
2.2.1
Stationarity
11
2.2.2
Sample Autocorrelations, Partial Autocorrelations,
and Spectral Densities
12
2.2.3
Data Transformations and Filters
17
2.3
Some Popular Time Series Models
22
2.3.1
Autoregressive
Processes
22
2.3.2
Finite-Order Moving Average Processes
25
2.3.3
ARIMA Processes
27
2.3.4
Autoregressive
Conditional Heteroskedasticity
28
2.3.5
Deterministic Terms
30
2.4
Parameter Estimation
30
2.4.1
Estimation of
AR
Models
30
2.4.2
Estimation of ARM A Models
32
2.5
Model Specification
33
χ
Contents
2.5.1 AR Order
Specification Criteria
33
2.5.2
Specifying More General Models
35
2.6
Model Checking
40
2.6.1
Descriptive Analysis of the Residuals
40
2.6.2
Diagnostic Tests of the Residuals
43
2.6.3
Stability Analysis
47
2.7
Unit Root Tests
53
2.7.1
Augmented Dickey-Fuller (ADF) Tests
54
2.7.2
Schmidt-Phillips Tests
57
2.7.3
A Test for Processes with Level Shift
58
2.7.4
KPSSTest
63
2.7.5
Testing for Seasonal Unit Roots
65
2.8
Forecasting Univariate Time Series
70
2.9
Examples
73
2.9.1
German Consumption
73
2.9.2
Polish Productivity
78
2.10
Where to Go from Here
85
3
Vector
Autoregressive
and Vector Error Correction Models
86
Helmut Liitkepohl
3.1
Introduction
86
3.2
VARsandVECMs
88
3.2.1
The Models
88
3.2.2
Deterministic Terms
91
3.2.3
Exogenous Variables
92
3.3
Estimation
93
3.3.1
Estimation of an Unrestricted
VAR
93
3.3.2
Estimation of VECMs
96
3.3.3
Restricting the Error Correction Term
105
3.3.4
Estimation of Models with More General Restrictions
and Structural Forms
108
3.4
Model Specification
110
3.4.1
Determining the
Autoregressive
Order
110
3.4.2
Specifying the Cointegrating Rank
112
3.4.3
Choice of Deterministic Term
120
3.4.4
Testing Restrictions Related to the
Cointegration
Vectors and the Loading Matrix
121
3.4.5
Testing Restrictions for the Short-Run Parameters
and Fitting Subset Models
122
3.5
Model Checking
125
3.5.1
Descriptive Analysis of the Residuals
125
3.5.2
Diagnostic Tests
127
3.5.3
Stability Analysis
131
Contents xi
3.6
Forecasting
VAR
Processes and VECMs
140
3.6.1
Known Processes
141
3.6.2
Estimated Processes
143
3.7
Granger-Causality Analysis
144
3.7.1
The Concept
144
3.7.2
Testing for Granger-Causality
148
3.8
An Example
150
3.9
Extensions
158
4
Structural Vector
Autoregressive
Modeling and Impulse
Responses
159
Jörg Breitung, Ralf Brüggemann, and Helmut Lütkepohl
4.1
Introduction
159
4.2 The Models 161
4.3 Impulse
Response Analysis
165
4.3.1
Stationary
VAR
Processes
165
4.3.2
Impulse Response Analysis of Nonstationary VARs
and VECMs
167
4.4
Estimation of Structural Parameters
172
4.4.1
SVAR
Models
172
4.4.2
Structural VECMs
175
4.5
Statistical Inference for Impulse Responses
176
4.5.1
Asymptotic Estimation Theory
176
4.5.2
Bootstrapping Impulse Responses
177
4.5.3
An Illustration
179
4.6
Forecast Error Variance Decomposition
180
4.7
Examples
183
4.7.1
A Simple AB-Model
183
4.7.2
The Blanchard-Quah Model
185
4.7.3
An SVECM for Canadian Labor Market Data
188
4.8
Conclusions
195
5
Conditional Heteroskedasticity
197
Helmut Herwartz
5.1
Stylized Facts of Empirical Price Processes
197
5.2
Univariate GARCH Models
198
5.2.1
Basic Features of GARCH Processes
199
5.2.2
Estimation of
G
ARCH Processes
201
5.2.3
Extensions
203
5.2.4
Blockdiagonality of the Information Matrix
206
5.2.5
Specification Testing
207
5.2.6
An Empirical Illustration with Exchange Rates
207
5.3
Multivariate GARCH Models
, 212
xii Contents
5.3.1 Alternative Model
Spécifications
214
5.3.2
Estimation
of
Multivariate GARCH Models 217
5.3.3
Extensions
218
5.3.4
Continuing the Empirical Illustration
220
6
Smooth Transition Regression Modeling
222
Timo
Teräsvirta
6.1
Introduction
222
6.2
The Model
222
6.3
The Modeling Cycle
225
6.3.1
Specification
225
6.3.2
Estimation of Parameters
228
6.3.3
Evaluation
229
6.4
Two Empirical Examples
234
6.4.1
Chemical Data
234
6.4.2
Demand for Money (M
1 )
in Germany
238
6.5
Final Remarks
242
7
Nonparametric Time Series Modeling
243
Rolf Tschernig
7.1
Introduction
243
7.2
Local Linear Estimation
245
7.2.1
The Estimators
245
7.2.2
Asymptotic Properties
248
7.2.3
Confidence Intervals
250
7.2.4
Plotting the Estimated Function
251
7.2.5
Forecasting
254
7.3
Bandwidth and Lag Selection
254
7.3.1
Bandwidth Estimation
256
7.3.2
Lag Selection
258
7.3.3
Illustration
261
7.4
Diagnostics
262
7.5
Modeling the Conditional Volatility
263
7.5.1
Estimation
264
7.5.2
Bandwidth Choice
265
7.5.3
Lag Selection
266
7.5.4
ARCH Errors
267
7.6
Local Linear Seasonal Modeling
268
7.6.1
The Seasonal Nonlinear
Autoregressive
Model
269
7.6.2
The Seasonal Dummy Nonlinear
Autoregressive
Model
270
7.6.3
Seasonal Shift Nonlinear
Autoregressive
Model
271
Contents xiii
7.7
Example I: Average Weekly Working Hours in the United
States
272
7.8
Example II: XETRA
Dax
Index
280
8
The Software
ЗМиїТі
289
Markus Krätzig
8.1
Introduction to
3
Mul
Ті
289
8.1.1
Software Concept
289
8.1.2
Operating
ЗМиїТі
290
8.2
Numbers, Dates, and Variables in
ЗМиїТі
290
8.2.1
Numbers
290
8.2.2
Numbers in Tables
291
8.2.3
Dates
291
8.2.4
Variable Names
292
8.3
Handling Data Sets
292
8.3.1
Importing Data
292
8.3.2
Excel Format
292
8.3.3
ASCII Format
293
8.3.4
ЗМиїТі
.dat
Format
293
8.4
Selecting, Transforming, and Creating Time Series
293
8.4.1
Time Series Selector
293
8.4.2
Time Series Calculator
295
8.5
Managing Variables in
ЗМиїТі
296
8.6
Notes for Econometric Software Developers
296
8.6.1
General Remark
296
8.6.2
TheJStatCom Framework
297
8.6.3
Component Structure
297
8.7
Conclusion
299
References
301
Index
317
|
| adam_txt |
Contents
Preface
page
xv
Notation and Abbreviations
xix
List of Contributors
xxv
1
Initial Tasks and Overview
1
Helmut Liitkepohl
1.1
Introduction
1
1.2
Setting Up an Econometric Project
2
1.3
Getting Data
3
1.4
Data Handling
5
1.5
Outline of Chapters
5
2
Univariate Time Series Analysis
8
Helmut Liitkepohl
2.1
Characteristics of Time Series
8
2.2
Stationary and Integrated Stochastic Processes
11
2.2.1
Stationarity
11
2.2.2
Sample Autocorrelations, Partial Autocorrelations,
and Spectral Densities
12
2.2.3
Data Transformations and Filters
17
2.3
Some Popular Time Series Models
22
2.3.1
Autoregressive
Processes
22
2.3.2
Finite-Order Moving Average Processes
25
2.3.3
ARIMA Processes
27
2.3.4
Autoregressive
Conditional Heteroskedasticity
28
2.3.5
Deterministic Terms
30
2.4
Parameter Estimation
30
2.4.1
Estimation of
AR
Models
30
2.4.2
Estimation of ARM A Models
32
2.5
Model Specification
33
χ
Contents
2.5.1 AR Order
Specification Criteria
33
2.5.2
Specifying More General Models
35
2.6
Model Checking
40
2.6.1
Descriptive Analysis of the Residuals
40
2.6.2
Diagnostic Tests of the Residuals
43
2.6.3
Stability Analysis
47
2.7
Unit Root Tests
53
2.7.1
Augmented Dickey-Fuller (ADF) Tests
54
2.7.2
Schmidt-Phillips Tests
57
2.7.3
A Test for Processes with Level Shift
58
2.7.4
KPSSTest
63
2.7.5
Testing for Seasonal Unit Roots
65
2.8
Forecasting Univariate Time Series
70
2.9
Examples
73
2.9.1
German Consumption
73
2.9.2
Polish Productivity
78
2.10
Where to Go from Here
85
3
Vector
Autoregressive
and Vector Error Correction Models
86
Helmut Liitkepohl
3.1
Introduction
86
3.2
VARsandVECMs
88
3.2.1
The Models
88
3.2.2
Deterministic Terms
91
3.2.3
Exogenous Variables
92
3.3
Estimation
93
3.3.1
Estimation of an Unrestricted
VAR
93
3.3.2
Estimation of VECMs
96
3.3.3
Restricting the Error Correction Term
105
3.3.4
Estimation of Models with More General Restrictions
and Structural Forms
108
3.4
Model Specification
110
3.4.1
Determining the
Autoregressive
Order
110
3.4.2
Specifying the Cointegrating Rank
112
3.4.3
Choice of Deterministic Term
120
3.4.4
Testing Restrictions Related to the
Cointegration
Vectors and the Loading Matrix
121
3.4.5
Testing Restrictions for the Short-Run Parameters
and Fitting Subset Models
122
3.5
Model Checking
125
3.5.1
Descriptive Analysis of the Residuals
125
3.5.2
Diagnostic Tests
127
3.5.3
Stability Analysis
131
Contents xi
3.6
Forecasting
VAR
Processes and VECMs
140
3.6.1
Known Processes
141
3.6.2
Estimated Processes
143
3.7
Granger-Causality Analysis
144
3.7.1
The Concept
144
3.7.2
Testing for Granger-Causality
148
3.8
An Example
150
3.9
Extensions
158
4
Structural Vector
Autoregressive
Modeling and Impulse
Responses
159
Jörg Breitung, Ralf Brüggemann, and Helmut Lütkepohl
4.1
Introduction
159
4.2 The Models 161
4.3 Impulse
Response Analysis
165
4.3.1
Stationary
VAR
Processes
165
4.3.2
Impulse Response Analysis of Nonstationary VARs
and VECMs
167
4.4
Estimation of Structural Parameters
172
4.4.1
SVAR
Models
172
4.4.2
Structural VECMs
175
4.5
Statistical Inference for Impulse Responses
176
4.5.1
Asymptotic Estimation Theory
176
4.5.2
Bootstrapping Impulse Responses
177
4.5.3
An Illustration
179
4.6
Forecast Error Variance Decomposition
180
4.7
Examples
183
4.7.1
A Simple AB-Model
183
4.7.2
The Blanchard-Quah Model
185
4.7.3
An SVECM for Canadian Labor Market Data
188
4.8
Conclusions
195
5
Conditional Heteroskedasticity
197
Helmut Herwartz
5.1
Stylized Facts of Empirical Price Processes
197
5.2
Univariate GARCH Models
198
5.2.1
Basic Features of GARCH Processes
199
5.2.2
Estimation of
G
ARCH Processes
201
5.2.3
Extensions
203
5.2.4
Blockdiagonality of the Information Matrix
206
5.2.5
Specification Testing
207
5.2.6
An Empirical Illustration with Exchange Rates
207
5.3
Multivariate GARCH Models
, 212
xii Contents
5.3.1 Alternative Model
Spécifications
214
5.3.2
Estimation
of
Multivariate GARCH Models 217
5.3.3
Extensions
218
5.3.4
Continuing the Empirical Illustration
220
6
Smooth Transition Regression Modeling
222
Timo
Teräsvirta
6.1
Introduction
222
6.2
The Model
222
6.3
The Modeling Cycle
225
6.3.1
Specification
225
6.3.2
Estimation of Parameters
228
6.3.3
Evaluation
229
6.4
Two Empirical Examples
234
6.4.1
Chemical Data
234
6.4.2
Demand for Money (M
1 )
in Germany
238
6.5
Final Remarks
242
7
Nonparametric Time Series Modeling
243
Rolf Tschernig
7.1
Introduction
243
7.2
Local Linear Estimation
245
7.2.1
The Estimators
245
7.2.2
Asymptotic Properties
248
7.2.3
Confidence Intervals
250
7.2.4
Plotting the Estimated Function
251
7.2.5
Forecasting
254
7.3
Bandwidth and Lag Selection
254
7.3.1
Bandwidth Estimation
256
7.3.2
Lag Selection
258
7.3.3
Illustration
261
7.4
Diagnostics
262
7.5
Modeling the Conditional Volatility
263
7.5.1
Estimation
264
7.5.2
Bandwidth Choice
265
7.5.3
Lag Selection
266
7.5.4
ARCH Errors
267
7.6
Local Linear Seasonal Modeling
268
7.6.1
The Seasonal Nonlinear
Autoregressive
Model
269
7.6.2
The Seasonal Dummy Nonlinear
Autoregressive
Model
270
7.6.3
Seasonal Shift Nonlinear
Autoregressive
Model
271
Contents xiii
7.7
Example I: Average Weekly Working Hours in the United
States
272
7.8
Example II: XETRA
Dax
Index
280
8
The Software
ЗМиїТі
289
Markus Krätzig
8.1
Introduction to
3
Mul
Ті
289
8.1.1
Software Concept
289
8.1.2
Operating
ЗМиїТі
290
8.2
Numbers, Dates, and Variables in
ЗМиїТі
290
8.2.1
Numbers
290
8.2.2
Numbers in Tables
291
8.2.3
Dates
291
8.2.4
Variable Names
292
8.3
Handling Data Sets
292
8.3.1
Importing Data
292
8.3.2
Excel Format
292
8.3.3
ASCII Format
293
8.3.4
ЗМиїТі
.dat
Format
293
8.4
Selecting, Transforming, and Creating Time Series
293
8.4.1
Time Series Selector
293
8.4.2
Time Series Calculator
295
8.5
Managing Variables in
ЗМиїТі
296
8.6
Notes for Econometric Software Developers
296
8.6.1
General Remark
296
8.6.2
TheJStatCom Framework
297
8.6.3
Component Structure
297
8.7
Conclusion
299
References
301
Index
317 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author_GND | (DE-588)10979544X |
| building | Verbundindex |
| bvnumber | BV023393521 |
| classification_rvk | QH 237 |
| ctrlnum | (OCoLC)441780034 (DE-599)BVBBV023393521 |
| 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 11 551955 |
| dewey-tens | 330 - Economics |
| discipline | Wirtschaftswissenschaften |
| discipline_str_mv | Wirtschaftswissenschaften |
| edition | 1. publ., transf. to digital print. |
| format | Book |
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| id | DE-604.BV023393521 |
| illustrated | Illustrated |
| index_date | 2024-07-02T21:20:59Z |
| indexdate | 2024-07-09T21:17:36Z |
| institution | BVB |
| isbn | 052183919X 9780521839198 0521547873 9780521547871 |
| language | English |
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| publisher | Cambridge Univ. Press |
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| spelling | Applied time series econometrics ed. by Helmut Lütkepohl ... 1. publ., transf. to digital print. Cambridge [u.a.] Cambridge Univ. Press 2007 XXV, 323 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Themes in modern econometrics Literaturverz. S. 301 - 315 Time series econometrics is a rapidly evolving field. In particular, the cointegration revolution has had a substantial impact on applied analysis. As a consequence of the fast pace of development there are no textbooks that cover the full range of methods in current use and explain how to proceed in applied domains. This gap in the literature motivates the present volume. The methods are sketched out briefly to remind the reader of the ideas underlying them and to give sufficient background for empirical work. The treatment can also be used as a textbook for a course on applied time series econometrics. The coverage of topics follows recent methodological developments. Unit root and cointegration analysis play a central part. Other topics include structural vector autoregressions, conditional heteroskedasticity and nonlinear and nonparametric time series models. A crucial component in empirical work is the software that is available for analysis. New methodologyis typically only gradually incorporated into the existing softwarepackages. Therefore a felxible Java interface has been created that allows readers to replicate the applications and conduct their own analyses. Mathematisches Modell Econometrics Time-series analysis Mathematical models Ökonometrie (DE-588)4132280-0 gnd rswk-swf Ökonometrisches Modell (DE-588)4043212-9 gnd rswk-swf Zeitreihenanalyse (DE-588)4067486-1 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Ökonometrie (DE-588)4132280-0 s Zeitreihenanalyse (DE-588)4067486-1 s DE-604 Ökonometrisches Modell (DE-588)4043212-9 s 1\p DE-604 Lütkepohl, Helmut 1951- Sonstige (DE-588)10979544X oth Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016576423&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
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| subject_GND | (DE-588)4132280-0 (DE-588)4043212-9 (DE-588)4067486-1 (DE-588)4143413-4 |
| title | Applied time series econometrics |
| title_auth | Applied time series econometrics |
| title_exact_search | Applied time series econometrics |
| title_exact_search_txtP | Applied time series econometrics |
| title_full | Applied time series econometrics ed. by Helmut Lütkepohl ... |
| title_fullStr | Applied time series econometrics ed. by Helmut Lütkepohl ... |
| title_full_unstemmed | Applied time series econometrics ed. by Helmut Lütkepohl ... |
| title_short | Applied time series econometrics |
| title_sort | applied time series econometrics |
| topic | Mathematisches Modell Econometrics Time-series analysis Mathematical models Ökonometrie (DE-588)4132280-0 gnd Ökonometrisches Modell (DE-588)4043212-9 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd |
| topic_facet | Mathematisches Modell Econometrics Time-series analysis Mathematical models Ökonometrie Ökonometrisches Modell Zeitreihenanalyse Aufsatzsammlung |
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