High-dimensional econometrics and identification:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New Jersey ; London ; Singapore ; Beijing ; Shanghai ; Hong Kong ; Taipei ; Chennai ; Tokyo
World Scientific
[2019]
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xiii, 164 Seiten Illustrationen, Diagramme |
| ISBN: | 9789811200151 |
Internformat
MARC
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Datensatz im Suchindex
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| adam_text | Contents Preface v About the Authors ix 1. Panel Data Model with Stationary and Nonstationary Regressors and Error Terms 1.1 1.2 1.3 1.4 1.5 1.6 Spurious Regression ............................................................. 1.1.1 Time-series spurious regression............................... 1.1.2 Panel spurious regression........................................ Model with Stationary and Nonstationary Regressors and Error Terms................................................................... 1.2.1 OLS estimator.......................................................... 1.2.2 FE estimator............................................................. 1.2.3 FD estimator............................................................. 1.2.4 GLS estimator.......................................................... 1.2.5 Feasible GLS estimator........................................... 1.2.6 Efficiency comparisons ........................................... Test of Hypotheses................................................................. 1.3.1 FE estimator............................................................. 1.3.2 FD estimator............................................................. 1.3.3 GLS estimator.......................................................... Conclusion............................................................................... Technical Proofs ................................................................... Exercises.................................................................................. ХІ 1 3 3 7 11 12 13 13 14 17 18 21 21 22 23 25
25 25
Contents 2. Panel Time Trend Model with Stationary and Nonstationary Error Terms 2.1 2.2 2.3 2.4 2.5 2.6 3. Estimation in a Time Trend Model..................................... 2.1.1 Estimation of autocorrelation parameter in a detrended model.............................................. 2.1.2 Estimation of autocorrelation parameter in a panel detrended model .................................................... Estimation in a Time Trend Model with Stationary and Nonstationary Error Terms........................................... 2.2.1 FE estimator............................................................. 2.2.2 FD estimator............................................................. 2.2.3 GLS estimator.......................................................... Test of Hypotheses................................................................ 2.3.1 FE estimator............................................................. 2.3.2 FD estimator............................................................. 2.3.3 The FE-FGLS estimator ........................................ Conclusion............................................................................... Technical Proofs ................................................................... Exercises.................................................................................. Estimation of Change Points in Stationary and Nonstationary Regressors and Error Term 3.1 3.2 3.3 3.4 Spurious Break...................................................................... 3.1.1 Spurious break in time-
series.................................. 3.1.2 Spurious break in panel data................................. When a Break Point Does Not Exist.................................. 3.2.1 OLS estimator.......................................................... 3.2.2 FD-based estimator................................................. When a Break Point Exists................................................. 3.3.1 Multiple regressions in time-series........................ 3.3.2 Multiple regressions in homogeneouspanel data............................................................................ 3.3.3 Multiple regressions in heterogeneous panel data............................................................................ Extensions............................................................................... 3.4.1 Change point estimation intime trend model ... 3.4.2 Panel model with stationary or nonstationary regressor and error term........................................... 35 36 36 39 42 43 43 44 45 45 46 47 51 51 52 57 58 58 63 67 67 71 71 71 74 77 79 79 81
Contents xiii 3.4.3 3.5 3.6 3.7 3.8 4. Weak Instruments in Panel Data Models 4.1 4.2 4.3 4.4 4.5 5. Change point estimation in panel data models with common factors....................................................... Testing for the Change Point.............................................. 3.5.1 OLS-based Wald statistic................................... 87 3.5.2 FGLS-based Wald statistic................................ 89 Conclusion............................................................................... Technical Proofs ................................................................... Exercises ............................................................................... 5.2 5.3 5.4 5.5 5.6 93 93 93 109 Weak IV Problem................................................................... 109 4.1.1 Weak IV in cross-sectional data........................... 109 4.1.2 Weak IV in panel data ........................................... 113 A General Framework.......................................................... 116 4.2.1 Within-group к-class panel data estimators .... 119 4.2.2 Wald test under weak identification................. 121 4.2.3 Testing for weak IVs................................................. 122 Conclusion............................................................................... 123 Technical Proofs ................................................................... 123 Exercises ............................................................................... 123 Incidental Parameters Problem in Panel Data Models 5.1 84 87 129 Incidental
Parameters Problem........................................... 130 5.1.1 An example of MLE with large n and finite T . . 130 5.1.2 An example of MLE with both large n and T . . . 132 Individual Effects ................................................................ 135 5.2.1 Fixed T................................................................. 136 5.2.2 Large T................................................................. 141 Additive Fixed Effects.......................................................... 144 Conclusion............................................................................... 146 Technical Proofs ................................................................... 146 Exercises ............................................................................... 146 Bibliography 155 Index 163
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| id | DE-604.BV046039574 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T08:33:31Z |
| institution | BVB |
| isbn | 9789811200151 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-031421295 |
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| physical | xiii, 164 Seiten Illustrationen, Diagramme |
| publishDate | 2019 |
| publishDateSearch | 2019 |
| publishDateSort | 2019 |
| publisher | World Scientific |
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| spelling | Kao, Chihwa Verfasser (DE-588)170413071 aut High-dimensional econometrics and identification Chihwa Kao (University of Connecticut, USA) and Long Liu (The University of Texas at San Antonio, USA) New Jersey ; London ; Singapore ; Beijing ; Shanghai ; Hong Kong ; Taipei ; Chennai ; Tokyo World Scientific [2019] © 2019 xiii, 164 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Ökonometrie (DE-588)4132280-0 gnd rswk-swf Identifikation (DE-588)4072712-9 gnd rswk-swf Panelanalyse (DE-588)4173172-4 gnd rswk-swf Stochastisches Signal (DE-588)4140374-5 gnd rswk-swf Econometrics Identification Ökonometrie (DE-588)4132280-0 s Panelanalyse (DE-588)4173172-4 s Stochastisches Signal (DE-588)4140374-5 s Identifikation (DE-588)4072712-9 s b DE-604 Liu, Long Verfasser (DE-588)137370008 aut Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031421295&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Kao, Chihwa Liu, Long High-dimensional econometrics and identification Ökonometrie (DE-588)4132280-0 gnd Identifikation (DE-588)4072712-9 gnd Panelanalyse (DE-588)4173172-4 gnd Stochastisches Signal (DE-588)4140374-5 gnd |
| subject_GND | (DE-588)4132280-0 (DE-588)4072712-9 (DE-588)4173172-4 (DE-588)4140374-5 |
| title | High-dimensional econometrics and identification |
| title_auth | High-dimensional econometrics and identification |
| title_exact_search | High-dimensional econometrics and identification |
| title_full | High-dimensional econometrics and identification Chihwa Kao (University of Connecticut, USA) and Long Liu (The University of Texas at San Antonio, USA) |
| title_fullStr | High-dimensional econometrics and identification Chihwa Kao (University of Connecticut, USA) and Long Liu (The University of Texas at San Antonio, USA) |
| title_full_unstemmed | High-dimensional econometrics and identification Chihwa Kao (University of Connecticut, USA) and Long Liu (The University of Texas at San Antonio, USA) |
| title_short | High-dimensional econometrics and identification |
| title_sort | high dimensional econometrics and identification |
| topic | Ökonometrie (DE-588)4132280-0 gnd Identifikation (DE-588)4072712-9 gnd Panelanalyse (DE-588)4173172-4 gnd Stochastisches Signal (DE-588)4140374-5 gnd |
| topic_facet | Ökonometrie Identifikation Panelanalyse Stochastisches Signal |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031421295&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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