Analysis of panel data:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Cambridge [u.a.]
Cambridge Univ. Press
2006
|
| Edition: | 2. ed., reprint. |
| Series: | Econometric Society monographs
34 |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis Klappentext |
| Item Description: | Includes bibliographical references and index |
| Physical Description: | XIV, 366 S. graph. Darst. |
| ISBN: | 9780521818551 9780521522717 |
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MARC
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| 100 | 1 | |a Hsiao, Cheng |d 1943- |e Verfasser |0 (DE-588)12424999X |4 aut | |
| 245 | 1 | 0 | |a Analysis of panel data |c Cheng Hsiao |
| 250 | |a 2. ed., reprint. | ||
| 264 | 1 | |a Cambridge [u.a.] |b Cambridge Univ. Press |c 2006 | |
| 300 | |a XIV, 366 S. |b graph. Darst. | ||
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| 490 | 1 | |a Econometric Society monographs |v 34 | |
| 500 | |a Includes bibliographical references and index | ||
| 650 | 4 | |a Ökonometrie - Panelanalyse - Varianzanalyse | |
| 650 | 4 | |a Analysis of variance | |
| 650 | 4 | |a Econometrics | |
| 650 | 4 | |a Panel analysis | |
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Record in the Search Index
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|---|---|
| adam_text | Contents
Preface
to the Second Edition Page
xiii
Preface to the First Edition
xv
Chapter
1.
Introduction
1
1.1
Advantages of Panel Data
1
1.2
Issues Involved in Utilizing Panel Data
8
1.2.1
Heterogeneity Bias
8
1.2.2
Selectivity Bias
9
1.3
Outline of the Monograph
11
Chapter
2.
Analysis of Covariance
14
2.1
Introduction
14
2.2
Analysis of Covariance
15
2.3
An Example
21
Chapter
3.
Simple Regression with Variable Intercepts
27
3.1
Introduction
27
3.2
Fixed-Effects Models: Least-Squares Dummy-Variable
Approach
30
3.3
Random-Effects Models: Estimation of
Variance-Components Models
34
3.3.1
Covariance Estimation
3 5
3.3.2
Generalized-Least-Squares Estimation
35
3.3.3
Maximum Likelihood Estimation
39
3.4
Fixed Effects or Random Effects
41
3.4.1
An Example
41
3.4.2
Conditional Inference or Unconditional (Marginal)
Inference
43
3.4.2.a Mundlak s Formulation
44
3.4.2.b Conditional and Unconditional Inferences in the
Presence or Absence of Correlation between
Individual Effects and Attributes
46
3.5 Tests
for Misspecification
49
3.6
Models with Specific Variables and Both Individual- and
Time-Specific Effects
51
3.6.1
Estimation of Models with Individual-Specific
Variables
51
3.6.2
Estimation of Models with Both Individual and
Time Effects
53
3.7
Heteroscedasticity
55
3.8
Models with Serially Correlated Errors
57
3.9
Models with Arbitrary Error Structure
-
Chamberlain
π
Approach
60
Appendix
ЗА:
Consistency and Asymptotic Normality of the
Minimum-Distance Estimator
65
Appendix 3B: Characteristic Vectors and the Inverse of the
Variance-Covariance Matrix of a
Three-Component Model
67
Chapter
4.
Dynamic Models with Variable Intercepts
69
4.1
Introduction
69
4.2
The Covariance Estimator
71
4.3
Random-Effects Models
73
4.3.1
Bias in the OLS Estimator
73
4.3.2
Model Formulation
75
4.3.3
Estimation of Random-Effects Models
78
4.3.
3.a Maximum Likelihood Estimator
78
4.3.3.b Generalized-Least-Squares Estimator
84
4.3.3.C Instrumental-Variable Estimator
85
4.3.3.d Generalized Method of Moments
Estimator
86
4.3.4
Testing Some Maintained Hypotheses on Initial
Conditions
90
4.3.5
Simulation Evidence
91
4.4
An Example
92
4.5
Fixed-Effects Models
95
4.5.1
Transformed Likelihood Approach
96
4.5.2
Minimum-Distance Estimator
98
4.5.3
Relations between the Likelihood-Based
Estimator and the Generalized Method of
Moments Estimator (GMM)
99
4.5.4
Random- versus Fixed-Effects Specification
101
4.6
Estimation of Dynamic Models with Arbitary Correlations
in the Residuals
103
4.7
Fixed-Effects Vector
Autoregressive
Models
105
4.7.1
Model Formulation
105
4.7.2
Generalized Method of Moments (GMM) Estimation
107
4.7.3
(Transformed) Maximum Likelihood Estimator
4.7.4
Minimum-Distance Estimator
Appendix 4A: Derivation of the Asymptotic Covariance Matrix
of the Feasible MDE
Chapter
5.
Simultaneous-Equations Models
5.1
Introduction
5.2
Joint Generalized-Least-Squares Estimation Technique
5.3
Estimation of Structural Equations
5.3.1
Estimation of a Single Equation in the Structural Model
5.3.2
Estimation of the Complete Structural System
5.4
Triangular System
5.4.1
Identification
5.4.2
Estimation
5.4.2.
a Instrumental-Variable Method
5.4.2.b Maximum-Likelihood Method
5.4.3
An Example
Appendix 5A
Chapter
6.
Variable-Coefficient Models
6.1
Introduction
6.2
Coefficients That Vary over Cross-Sectional Units
6.2.1
Fixed-Coefficient Model
6.2.2
Random-Coefficient Model
6.2.2.a
The Model
6.2.2.b Estimation
6.2.2.C Predicting Individual Coefficients
6.2.2.d Testing for Coefficient Variation
6.2.2.Є
Fixed or Random Coefficients
6.2.2.
f
An Example
6.3
Coefficients That Vary over Time and Cross-Sectional Units
6.3.1
The Model
6.3.2
Fixed-Coefficient Model
6.3.3
Random-Coefficient Model
6.4
Coefficients That Evolve over Time
6.4.1
The Model
6.4.2
Predicting
ßr
by the
Kalman
Filter
6.4.3
Maximum Likelihood Estimation
6.4.4
Tests for Parameter Constancy
6.5
Coefficients That Are Functions of Other Exogenous
Variables
6.6
A Mixed Fixed- and Random-Coefficients Model
6.6.1
Model Formulation
6.6.2
A Bayes
Solution
6.6.3
An Example
6.6.4
Random or Fixed Parameters
172
6.6.4.a An Example
172
6.6.4.b Model Selection
173
6.7
Dynamic Random-Coefficient Models
175
6.8
An Example
-
Liquidity Constraints and Firm Investment
Expenditure
180
Appendix 6A: Combination of Two Normal Distributions
185
Chapter
7.
Discrete Data
188
7.1
Introduction
188
7.2
Some Discrete-Response Models
188
7.3
Parametric Approach to Static Models with Heterogeneity
193
7.3.1
Fixed-Effects Models
194
7.3.1.
a Maximum Likelihood Estimator
194
7.3.1
.b Conditions for the Existence of a Consistent
Estimator
195
7.3.
l.c Some Monte Carlo Evidence
198
7.3.2
Random-Effects Models
199
7.4
Semiparametric Approach to Static Models
202
7.4.1
Maximum Score Estimator
203
7.4.2
A Root-N Consistent Semiparametric Estimator
205
7.5
Dynamic Models
206
7.5.1
The General Model
206
7.5.2
Initial Conditions
208
7.5.3
A Conditional Approach
211
7.5.4
State Dependence versus Heterogeneity
216
7.5.5
Two Examples
218
7.5.5.a
Female Employment
218
7.5.5.b Household Brand Choices
221
Chapter
8.
Truncated and Censored Data
225
8.1
Introduction
225
8.2
An Example
-
Nonrandomly Missing Data
234
8.2.1
Introduction
234
8.2.2
A Probability Model of Attrition and Selection Bias
235
8.2.3
Attrition in the Gary Income-Maintenance Experiment
238
8.3
Tobit Models with Random Individual Effects
240
8.4
Fixed-Effects Estimator
243
8.4.1
Pairwise Trimmed Least-Squares and
Least-Absolute-Deviation Estimators for
Truncated and Censored Regressions
243
8.4.
La Truncated Regression
243
8.4.1.
b
Censored Regressions
249
8.4.2
A Semiparametric Two-Step Estimator for the
Endogenously Determined Sample Selection Model
253
8.5
An Example: Housing Expenditure
255
8.6
Dynamic Tobit Models
259
8.6.1
Dynamic Censored Models
259
8.6.2
Dynamic Sample Selection Models
265
Chapter
9.
Incomplete Panel Data
268
9.1
Estimating Distributed Lags in Short Panels
268
9.1.1
Introduction
268
9.1.2
Common Assumptions
270
9.1.3
Identification Using Prior Structure of the Process
of the Exogenous Variable
271
9.1.4
Identification Using Prior Structure of the Lag
Coefficients
275
9.1.5
Estimation and Testing
277
9.2
Rotating or Randomly Missing Data
279
9.3
Pseudopanels
(or Repeated Cross-Sectional
Data)
283
9.4
Pooling of a Single Cross-Sectional and a Single
Time-Series Data Set
285
9.4.1
Introduction
285
9.4.2
The Likelihood Approach to Pooling Cross-Sectional
and Time-Series Data
287
9.4.3
An Example
288
Chapter
10.
Miscellaneous Topics
291
10.1
Simulation Methods
291
10.2
Panels with Large
N
and
Τ
295
10.3
Unit-Root Tests
298
10.4
Data with Multilevel Structures
302
10.5
Errors of Measurement
304
10.6
Modeling Cross-Sectional Dependence
309
Chapter
11.
A Summary View
311
11.1
Introduction
311
11.2
Benefits and Limitations of Panel Data
311
11.2.1
Increasing Degrees of Freedom and Lessening
the Problem of Multicollinearity
311
11.2.2
Identification and Discrimination between
Competing Hypotheses
312
11.2.3
Reducing Estimation Bias
313
11.2.3.
a Omitted-Variable Bias
313
1
1.2.3.b Bias Induced by the Dynamic Structure
of a Model
315
11.2.3.C Simultaneity Bias
316
11.2.3
.d Bias Induced by Measurement Errors
316
11.2.4
Providing Micro Foundations for Aggregate Data
Analysis
316
11.3
Efficiency of the Estimates
317
Notes
319
References
331
Author Index
353
Subject Index
359
Panel
data models have become increasingly popular among applied researchers due to their
heightened capacity for capturing the complexity of human behavior as compared to cross-
sectional or time-series data models. As a consequence, more and richer panel data sets also
have become increasingly available. This second edition is a substantial revision of the highly
successful first edition of
1986.
Recent advances in panel data research are presented in a
rigorous and accessible manner and are carefully integrated with the older material. The thorough
discussion of theory and the judicious use of empirical examples make this book useful to graduate
students and advanced researchers in economics, business, sociology, political science, etc.
Other specific revisions include the introduction of the
Bayes
method and the notion of strict
exogeneity with estimators presented in a generalized method of moments framework to link the
identification of various models, intuitive explanations of semiparametric methods of estimating
discrete choice models and methods of pairwise trimming for the estimation of panel sample
selection models, etc.
|
| adam_txt |
Contents
Preface
to the Second Edition Page
xiii
Preface to the First Edition
xv
Chapter
1.
Introduction
1
1.1
Advantages of Panel Data
1
1.2
Issues Involved in Utilizing Panel Data
8
1.2.1
Heterogeneity Bias
8
1.2.2
Selectivity Bias
9
1.3
Outline of the Monograph
11
Chapter
2.
Analysis of Covariance
14
2.1
Introduction
14
2.2
Analysis of Covariance
15
2.3
An Example
21
Chapter
3.
Simple Regression with Variable Intercepts
27
3.1
Introduction
27
3.2
Fixed-Effects Models: Least-Squares Dummy-Variable
Approach
30
3.3
Random-Effects Models: Estimation of
Variance-Components Models
34
3.3.1
Covariance Estimation
3 5
3.3.2
Generalized-Least-Squares Estimation
35
3.3.3
Maximum Likelihood Estimation
39
3.4
Fixed Effects or Random Effects
41
3.4.1
An Example
41
3.4.2
Conditional Inference or Unconditional (Marginal)
Inference
43
3.4.2.a Mundlak's Formulation
44
3.4.2.b Conditional and Unconditional Inferences in the
Presence or Absence of Correlation between
Individual Effects and Attributes
46
3.5 Tests
for Misspecification
49
3.6
Models with Specific Variables and Both Individual- and
Time-Specific Effects
51
3.6.1
Estimation of Models with Individual-Specific
Variables
51
3.6.2
Estimation of Models with Both Individual and
Time Effects
53
3.7
Heteroscedasticity
55
3.8
Models with Serially Correlated Errors
57
3.9
Models with Arbitrary Error Structure
-
Chamberlain
π
Approach
60
Appendix
ЗА:
Consistency and Asymptotic Normality of the
Minimum-Distance Estimator
65
Appendix 3B: Characteristic Vectors and the Inverse of the
Variance-Covariance Matrix of a
Three-Component Model
67
Chapter
4.
Dynamic Models with Variable Intercepts
69
4.1
Introduction
69
4.2
The Covariance Estimator
71
4.3
Random-Effects Models
73
4.3.1
Bias in the OLS Estimator
73
4.3.2
Model Formulation
75
4.3.3
Estimation of Random-Effects Models
78
4.3.
3.a Maximum Likelihood Estimator
78
4.3.3.b Generalized-Least-Squares Estimator
84
4.3.3.C Instrumental-Variable Estimator
85
4.3.3.d Generalized Method of Moments
Estimator
86
4.3.4
Testing Some Maintained Hypotheses on Initial
Conditions
90
4.3.5
Simulation Evidence
91
4.4
An Example
92
4.5
Fixed-Effects Models
95
4.5.1
Transformed Likelihood Approach
96
4.5.2
Minimum-Distance Estimator
98
4.5.3
Relations between the Likelihood-Based
Estimator and the Generalized Method of
Moments Estimator (GMM)
99
4.5.4
Random- versus Fixed-Effects Specification
101
4.6
Estimation of Dynamic Models with Arbitary Correlations
in the Residuals
103
4.7
Fixed-Effects Vector
Autoregressive
Models
105
4.7.1
Model Formulation
105
4.7.2
Generalized Method of Moments (GMM) Estimation
107
4.7.3
(Transformed) Maximum Likelihood Estimator
4.7.4
Minimum-Distance Estimator
Appendix 4A: Derivation of the Asymptotic Covariance Matrix
of the Feasible MDE
Chapter
5.
Simultaneous-Equations Models
5.1
Introduction
5.2
Joint Generalized-Least-Squares Estimation Technique
5.3
Estimation of Structural Equations
5.3.1
Estimation of a Single Equation in the Structural Model
5.3.2
Estimation of the Complete Structural System
5.4
Triangular System
5.4.1
Identification
5.4.2
Estimation
5.4.2.
a Instrumental-Variable Method
5.4.2.b Maximum-Likelihood Method
5.4.3
An Example
Appendix 5A
Chapter
6.
Variable-Coefficient Models
6.1
Introduction
6.2
Coefficients That Vary over Cross-Sectional Units
6.2.1
Fixed-Coefficient Model
6.2.2
Random-Coefficient Model
6.2.2.a
The Model
6.2.2.b Estimation
6.2.2.C Predicting Individual Coefficients
6.2.2.d Testing for Coefficient Variation
6.2.2.Є
Fixed or Random Coefficients
6.2.2.
f
An Example
6.3
Coefficients That Vary over Time and Cross-Sectional Units
6.3.1
The Model
6.3.2
Fixed-Coefficient Model
6.3.3
Random-Coefficient Model
6.4
Coefficients That Evolve over Time
6.4.1
The Model
6.4.2
Predicting
ßr
by the
Kalman
Filter
6.4.3
Maximum Likelihood Estimation
6.4.4
Tests for Parameter Constancy
6.5
Coefficients That Are Functions of Other Exogenous
Variables
6.6
A Mixed Fixed- and Random-Coefficients Model
6.6.1
Model Formulation
6.6.2
A Bayes
Solution
6.6.3
An Example
6.6.4
Random or Fixed Parameters
172
6.6.4.a An Example
172
6.6.4.b Model Selection
173
6.7
Dynamic Random-Coefficient Models
175
6.8
An Example
-
Liquidity Constraints and Firm Investment
Expenditure
180
Appendix 6A: Combination of Two Normal Distributions
185
Chapter
7.
Discrete Data
188
7.1
Introduction
188
7.2
Some Discrete-Response Models
188
7.3
Parametric Approach to Static Models with Heterogeneity
193
7.3.1
Fixed-Effects Models
194
7.3.1.
a Maximum Likelihood Estimator
194
7.3.1
.b Conditions for the Existence of a Consistent
Estimator
195
7.3.
l.c Some Monte Carlo Evidence
198
7.3.2
Random-Effects Models
199
7.4
Semiparametric Approach to Static Models
202
7.4.1
Maximum Score Estimator
203
7.4.2
A Root-N Consistent Semiparametric Estimator
205
7.5
Dynamic Models
206
7.5.1
The General Model
206
7.5.2
Initial Conditions
208
7.5.3
A Conditional Approach
211
7.5.4
State Dependence versus Heterogeneity
216
7.5.5
Two Examples
218
7.5.5.a
Female Employment
218
7.5.5.b Household Brand Choices
221
Chapter
8.
Truncated and Censored Data
225
8.1
Introduction
225
8.2
An Example
-
Nonrandomly Missing Data
234
8.2.1
Introduction
234
8.2.2
A Probability Model of Attrition and Selection Bias
235
8.2.3
Attrition in the Gary Income-Maintenance Experiment
238
8.3
Tobit Models with Random Individual Effects
240
8.4
Fixed-Effects Estimator
243
8.4.1
Pairwise Trimmed Least-Squares and
Least-Absolute-Deviation Estimators for
Truncated and Censored Regressions
243
8.4.
La Truncated Regression
243
8.4.1.
b
Censored Regressions
249
8.4.2
A Semiparametric Two-Step Estimator for the
Endogenously Determined Sample Selection Model
253
8.5
An Example: Housing Expenditure
255
8.6
Dynamic Tobit Models
259
8.6.1
Dynamic Censored Models
259
8.6.2
Dynamic Sample Selection Models
265
Chapter
9.
Incomplete Panel Data
268
9.1
Estimating Distributed Lags in Short Panels
268
9.1.1
Introduction
268
9.1.2
Common Assumptions
270
9.1.3
Identification Using Prior Structure of the Process
of the Exogenous Variable
271
9.1.4
Identification Using Prior Structure of the Lag
Coefficients
275
9.1.5
Estimation and Testing
277
9.2
Rotating or Randomly Missing Data
279
9.3
Pseudopanels
(or Repeated Cross-Sectional
Data)
283
9.4
Pooling of a Single Cross-Sectional and a Single
Time-Series Data Set
285
9.4.1
Introduction
285
9.4.2
The Likelihood Approach to Pooling Cross-Sectional
and Time-Series Data
287
9.4.3
An Example
288
Chapter
10.
Miscellaneous Topics
291
10.1
Simulation Methods
291
10.2
Panels with Large
N
and
Τ
295
10.3
Unit-Root Tests
298
10.4
Data with Multilevel Structures
302
10.5
Errors of Measurement
304
10.6
Modeling Cross-Sectional Dependence
309
Chapter
11.
A Summary View
311
11.1
Introduction
311
11.2
Benefits and Limitations of Panel Data
311
11.2.1
Increasing Degrees of Freedom and Lessening
the Problem of Multicollinearity
311
11.2.2
Identification and Discrimination between
Competing Hypotheses
312
11.2.3
Reducing Estimation Bias
313
11.2.3.
a Omitted-Variable Bias
313
1
1.2.3.b Bias Induced by the Dynamic Structure
of a Model
315
11.2.3.C Simultaneity Bias
316
11.2.3
.d Bias Induced by Measurement Errors
316
11.2.4
Providing Micro Foundations for Aggregate Data
Analysis
316
11.3
Efficiency of the Estimates
317
Notes
319
References
331
Author Index
353
Subject Index
359
Panel
data models have become increasingly popular among applied researchers due to their
heightened capacity for capturing the complexity of human behavior as compared to cross-
sectional or time-series data models. As a consequence, more and richer panel data sets also
have become increasingly available. This second edition is a substantial revision of the highly
successful first edition of
1986.
Recent advances in panel data research are presented in a
rigorous and accessible manner and are carefully integrated with the older material. The thorough
discussion of theory and the judicious use of empirical examples make this book useful to graduate
students and advanced researchers in economics, business, sociology, political science, etc.
Other specific revisions include the introduction of the
Bayes
method and the notion of strict
exogeneity with estimators presented in a generalized method of moments framework to link the
identification of various models, intuitive explanations of semiparametric methods of estimating
discrete choice models and methods of pairwise trimming for the estimation of panel sample
selection models, etc. |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Hsiao, Cheng 1943- |
| author_GND | (DE-588)12424999X |
| author_facet | Hsiao, Cheng 1943- |
| author_role | aut |
| author_sort | Hsiao, Cheng 1943- |
| author_variant | c h ch |
| building | Verbundindex |
| bvnumber | BV023316753 |
| classification_rvk | QH 237 QH 300 |
| classification_tum | MAT 900f |
| ctrlnum | (OCoLC)254645070 (DE-599)BVBBV023316753 |
| dewey-full | 330.015195 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 330 - Economics |
| dewey-raw | 330.015195 |
| dewey-search | 330.015195 |
| dewey-sort | 3330.015195 |
| dewey-tens | 330 - Economics |
| discipline | Mathematik Wirtschaftswissenschaften |
| discipline_str_mv | Mathematik Wirtschaftswissenschaften |
| edition | 2. ed., reprint. |
| format | Book |
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| id | DE-604.BV023316753 |
| illustrated | Illustrated |
| index_date | 2024-07-02T20:52:23Z |
| indexdate | 2024-07-09T21:15:43Z |
| institution | BVB |
| isbn | 9780521818551 9780521522717 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016500920 |
| oclc_num | 254645070 |
| open_access_boolean | |
| owner | DE-739 |
| owner_facet | DE-739 |
| physical | XIV, 366 S. graph. Darst. |
| publishDate | 2006 |
| publishDateSearch | 2006 |
| publishDateSort | 2006 |
| publisher | Cambridge Univ. Press |
| record_format | marc |
| series | Econometric Society monographs |
| series2 | Econometric Society monographs |
| spelling | Hsiao, Cheng 1943- Verfasser (DE-588)12424999X aut Analysis of panel data Cheng Hsiao 2. ed., reprint. Cambridge [u.a.] Cambridge Univ. Press 2006 XIV, 366 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Econometric Society monographs 34 Includes bibliographical references and index Ökonometrie - Panelanalyse - Varianzanalyse Analysis of variance Econometrics Panel analysis Statistik (DE-588)4056995-0 gnd rswk-swf Ökonometrie (DE-588)4132280-0 gnd rswk-swf Panelanalyse (DE-588)4173172-4 gnd rswk-swf Panelanalyse (DE-588)4173172-4 s Statistik (DE-588)4056995-0 s DE-604 Ökonometrie (DE-588)4132280-0 s 1\p DE-604 Econometric Society monographs 34 (DE-604)BV000018129 34 Digitalisierung UB Passau application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016500920&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=016500920&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Hsiao, Cheng 1943- Analysis of panel data Econometric Society monographs Ökonometrie - Panelanalyse - Varianzanalyse Analysis of variance Econometrics Panel analysis Statistik (DE-588)4056995-0 gnd Ökonometrie (DE-588)4132280-0 gnd Panelanalyse (DE-588)4173172-4 gnd |
| subject_GND | (DE-588)4056995-0 (DE-588)4132280-0 (DE-588)4173172-4 |
| title | Analysis of panel data |
| title_auth | Analysis of panel data |
| title_exact_search | Analysis of panel data |
| title_exact_search_txtP | Analysis of panel data |
| title_full | Analysis of panel data Cheng Hsiao |
| title_fullStr | Analysis of panel data Cheng Hsiao |
| title_full_unstemmed | Analysis of panel data Cheng Hsiao |
| title_short | Analysis of panel data |
| title_sort | analysis of panel data |
| topic | Ökonometrie - Panelanalyse - Varianzanalyse Analysis of variance Econometrics Panel analysis Statistik (DE-588)4056995-0 gnd Ökonometrie (DE-588)4132280-0 gnd Panelanalyse (DE-588)4173172-4 gnd |
| topic_facet | Ökonometrie - Panelanalyse - Varianzanalyse Analysis of variance Econometrics Panel analysis Statistik Ökonometrie Panelanalyse |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016500920&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=016500920&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV000018129 |
| work_keys_str_mv | AT hsiaocheng analysisofpaneldata |