Econometric models for industrial organization:
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| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
New Jersey
World Scientific
[2017]
|
| Series: | World scientific lecture notes in economics
volume 3 |
| Subjects: | |
| Online Access: | FHN01 FUBA1 Volltext Inhaltsverzeichnis |
| Physical Description: | 1 Online-Ressource (xiv, 139 Seiten) Illustrationen |
| ISBN: | 9789813109667 |
Staff View
MARC
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Record in the Search Index
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| adam_text | Titel: Econometric models for industrial organization
Autor: Shum, Matthew
Jahr: 2017
Contents
Preface v
Author s Biography vii
Acronyms ix
1. Demand Estimation for Differentiated-product
Markets 1
1.1 Why Demand Analysis/Estimation? ..................1
1.2 Review: Demand Estimation............................2
1.2.1 Traditional approach to demand
estimation ........................................3
1.3 Discrete-choice Approach to Modeling Demand ... 4
1.4 Berry (1994) Approach to Estimate Demand
in Differentiated Product Markets......................8
1.4.1 Measuring market power: Recovering
markups ..........................................14
1.4.2 Estimating cost function parameters............16
1.5 Berry, Levinsohn, and Bakes (1995):
Demand Estimation Using Random-coefficients
Logit Model...................... . 17
1.5.1 Simulating the integral in Eq. (1.4) ............21
1.6 Applications..............................................22
1.7 Additional Details: General Presentation
of Random Utility Models..............................24
Bibliography ..................................................26
2. Single-agent Dynamic Models: Part 1 29
2.1 Rust (1987)..............................................29
2.1.1 Behavioral model ................................29
2.1.2 Econometric model ..............................33
Bibliography..................................................38
3. Single-agent Dynamic Models: Part 2 39
3.1 Alternative Estimation Approaches: Estimating
Dynamic Optimization Models Without Numeric
Dynamic Programming..................................39
3.1.1 Notation: Hats and Tildes........................40
3.1.2 Estimation: Match Hats to Tildes..............43
3.1.3 A further shortcut in the discrete state case . . 43
3.2 Semiparametric Identification of DDC Models .... 46
3.3 Appendix: A Result for MNL Model ..................50
3.4 Appendix: Relations Between Different Value
Function Notions........................................52
Bibliography..................................................53
4. Single-agent Dynamic Models: Part 3 55
4.1 Model with Persistence in Unobservables
( Unobserved State Variables )........................55
4.1.1 Example: Pakes (1986) patent renewal
model..............................................55
4.1.2 Estimation: Likelihood function and
simulation ........................................58
4.1.3 Crude frequency simulator: Naive
approach.................................59
4.1.4 Importance sampling approach: Particle
filtering............................................60
4.1.5 Nonparametric identification of Markovian
Dynamic Discrete Choice (DDC) models
with unobserved state variables..................64
Bibliography..................................................71
5. Dynamic Games 73
5.1 Econometrics of Dynamic Oligopoly Models..........73
5.2 Theoretical Features ....................................74
5.2.1 Computation of dynamic equilibrium..........76
5.3 Games with Incomplete Information ................77
Bibliography ..................................................79
6. Auction Models 81
6.1 Parametric Estimation: Laffont-Ossard-Vuong
(1995) ................................81
6.2 Nonparametric Estimation: Guerre-Perrigne-Vuong
(2000) .......................................85
6.3 Affiliated Values Models................................88
6.3.1 Affiliated PV models ............................88
6.3.2 Common value models: Testing between
CV and PV........................................90
6.4 Haile—Tamer s Incomplete Model of English
Auctions..................................................92
Bibliography ..............................94
7. Partial Identification in Structural Models 95
7.1 Entry Games with Structural Errors..................96
7.1.1 Deriving moment inequalities....................98
7.2 Entry Games with Expectational Errors..............99
7.3 Inference Procedures with Moment Inequalities/
Incomplete Models......................................100
7.3.1 Identified parameter vs. identified set..........100
7.3.2 Confidence sets which cover identified
parameters ......................................101
7.3.3 Confidence sets which cover the
identified set......................................103
7.4 Random Set Approach .................................105
7.4.1 Application: Sharp identified region for games
with multiple equilibria..........................106
Bibliography ...................................107
8. Background: Simulation Methods 109
8.1 Importance Sampling....................................110
8.1.1 GHK simulator: Get draws from truncated
multivariate normal (MVN) distribution .... 110
8.1.2 Monte Carlo integration using the GHK
simulator..........................................113
8.1.3 Integrating over truncated (conditional)
distribution F{x a x b)......................114
8.2 Markov Chain Monte Carlo (MCMC) Simulation . . 115
8.2.1 Background: First-order Markov chains .... 116
8.2.2 Metropolis-Hastings approach..................117
8.2.3 Application to Bayesian posterior inference . . 120
Bibliography..................................................121
9. Problem Sets 123
Bibliography..................................................134
Index 135
|
| any_adam_object | 1 |
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| dewey-tens | 330 - Economics |
| discipline | Wirtschaftswissenschaften |
| format | Electronic eBook |
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| spelling | Shum, Matthew Verfasser (DE-588)128978597 aut Econometric models for industrial organization Matthew Shum, Caltech New Jersey World Scientific [2017] © 2017 1 Online-Ressource (xiv, 139 Seiten) Illustrationen txt rdacontent c rdamedia cr rdacarrier World scientific lecture notes in economics volume 3 Ökonometrisches Modell Industrial organization (Economic theory) Econometric models Industrieökonomie (DE-588)4133311-1 gnd rswk-swf Ökonometrisches Modell (DE-588)4043212-9 gnd rswk-swf Industrieökonomie (DE-588)4133311-1 s Ökonometrisches Modell (DE-588)4043212-9 s DE-604 Erscheint auch als Druck-Ausgabe 978-981-3109-65-0 World scientific lecture notes in economics volume 3 (DE-604)BV044244997 3 http://www.worldscientific.com/worldscibooks/10.1142/10033#t=toc Verlag Volltext HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029650270&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Shum, Matthew Econometric models for industrial organization World scientific lecture notes in economics Ökonometrisches Modell Industrial organization (Economic theory) Econometric models Industrieökonomie (DE-588)4133311-1 gnd Ökonometrisches Modell (DE-588)4043212-9 gnd |
| subject_GND | (DE-588)4133311-1 (DE-588)4043212-9 |
| title | Econometric models for industrial organization |
| title_auth | Econometric models for industrial organization |
| title_exact_search | Econometric models for industrial organization |
| title_full | Econometric models for industrial organization Matthew Shum, Caltech |
| title_fullStr | Econometric models for industrial organization Matthew Shum, Caltech |
| title_full_unstemmed | Econometric models for industrial organization Matthew Shum, Caltech |
| title_short | Econometric models for industrial organization |
| title_sort | econometric models for industrial organization |
| topic | Ökonometrisches Modell Industrial organization (Economic theory) Econometric models Industrieökonomie (DE-588)4133311-1 gnd Ökonometrisches Modell (DE-588)4043212-9 gnd |
| topic_facet | Ökonometrisches Modell Industrial organization (Economic theory) Econometric models Industrieökonomie |
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