Modeling and estimating income data in the presence of distinctive zero and heaped responses:
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Abschlussarbeit Buch |
| Sprache: | English |
| Veröffentlicht: |
Bamberg
März 2016
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| Schlagworte: | |
| Online-Zugang: | Volltext Inhaltsverzeichnis |
| Beschreibung: | xvi, 255 Seiten Illustrationen, Diagramme |
Internformat
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| 245 | 1 | 0 | |a Modeling and estimating income data in the presence of distinctive zero and heaped responses |c vorgelegt von Ariane Würbach, Magistra Artium |
| 264 | 1 | |a Bamberg |c März 2016 | |
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Datensatz im Suchindex
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| adam_text | Contents
List of Figures..................................................... iii
List of Tables....................................................... vi
List of Abbreviations................................................ ix
List of Symbols......................................................xii
1 Introduction and motivating example 1
1.1 Heaping as measurement error..................................... 7
1.1.1 Definitions and theoretical framework..................... 9
1.1.2 Heaping in income data ...................................17
1.1.3 Diagnostic tools for rounding and heaping................20
1.1.4 Coping with rounding and heaping..........................25
1.2 Motivating example...............................................35
1.2.1 Description of the NEPS income data.......................36
1.2.2 Multivariate consideration of income data and heaping be-
havior ..........................................................39
2 Modeling heaped income data 49
2.1 Latent distribution of true income values.......................49
2.2 Heaping mechanism...............................................51
2.3 Constraint system...............................................53
2.4 Log-Likelihood .................................................53
2.5 Specification of the heaping model and data generating process ... 55
3 Estimation of the heaping model 59
3.1 Frequentist estimation of the heaping model........................59
3.1.1 Maximum Likelihood with constraints .........................59
3.1.2 Specification and results of ML estimation...................60
3.2 Bayesian estimation of the heaping model ..........................62
3.2.1 Introduction to MCMC samplers................................63
3.2.2 The Metropolis-Hastings algorithm in general and the random-
walk Metropolis algorithm in specific......................64
3.2.3 Tuning of the original RWM algorithm.........................68
3.2.4 Different blocking strategies in the RWM algorithm...........68
3.2.5 Adaptive MCMC for a Gaussian proposal density................72
3.2.6 Tools for comparison of different RWM algorithms.............76
3.2.7 Specification and results of different RWM algorithms .... 82
l
11
Contents
3.3 A comparison of ML and RWM estimation of the heaping model . 103
3.3.1 Convergence of multiple independent chains.................103
3.3.2 Performance assessment.....................................105
3.3.3 Model selection by marginal likelihood.....................109
4 Modifications and extensions of the heaping model 113
4.1 Modifications of the heaping model..............................113
4.1.1 Modification with respect to the latent distribution.......114
4.1.2 Modifications of the heaping pattern.......................117
4.1.3 Modifications of the heaping mechanism.....................122
4.2 Extension of the heaping model to a multivariate context........130
4.2.1 Adding covariates to model income level...................130
4.2.2 Adding covariates to model income level and the heaping
mechanism..................................................134
5 Application of the heaping model to NEPS data 137
5.1 Apply alternative models to real data...........................137
5.2 Posterior predictive checks.......................................142
6 Conclusion 149
6.1 Limitations ......................................................152
6.2 Further research..................................................153
Literature 162
A Additional material 185
A.l Supplemental tables and figures....................................186
A. 2 Derivations and mathematics ......................................230
A.2.1 Moments of the log-normal distribution....................230
A.2.2 Moments of the Dagum distribution.........................231
A.2.3 Derivation of the inefficiency factor.......................232
A.2.4 Geweke’s (1992) test of non-stationarity..................234
A.2.5 Brooks and Gelman’s (1998) convergence criterion..........235
A. 2.6 Marginal likelihood estimation according to Chib and Jeli-
azkov (2001)............................................. 237
B Sources 241
B. l R Code for ML estimation..........................................241
B.2 R Code for RWM estimation..........................................244
B. 2.1 Log-Likelihood............................................244
B.2.2 Constraints...............................................245
B.2.3 RWM algorithm.............................................246
B.2.4 Call RWM settings.........................................252
B.3 R session information..............................................255
|
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| spelling | Würbach, Ariane 1981- Verfasser (DE-588)1120935318 aut Modeling and estimating income data in the presence of distinctive zero and heaped responses vorgelegt von Ariane Würbach, Magistra Artium Bamberg März 2016 xvi, 255 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Dissertation Otto-Friedrich-Universität Bamberg 2016 Häufigkeitsverteilung (DE-588)4158707-8 gnd rswk-swf Antwortverhalten (DE-588)4122814-5 gnd rswk-swf Einkommensverteilung (DE-588)4013898-7 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Einkommensverteilung (DE-588)4013898-7 s Häufigkeitsverteilung (DE-588)4158707-8 s Antwortverhalten (DE-588)4122814-5 s DE-604 Rässler, Susanne 1962-2018 (DE-588)171420314 dgs Otto-Friedrich-Universität Bamberg (DE-588)2056127-1 dgg Erscheint auch als Online-Ausgabe urn:nbn:de:bvb:473-opus4-477176 9841 KB https://fis.uni-bamberg.de/handle/uniba/41034 Verlag kostenfrei Volltext Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029343732&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Würbach, Ariane 1981- Modeling and estimating income data in the presence of distinctive zero and heaped responses Häufigkeitsverteilung (DE-588)4158707-8 gnd Antwortverhalten (DE-588)4122814-5 gnd Einkommensverteilung (DE-588)4013898-7 gnd |
| subject_GND | (DE-588)4158707-8 (DE-588)4122814-5 (DE-588)4013898-7 (DE-588)4113937-9 |
| title | Modeling and estimating income data in the presence of distinctive zero and heaped responses |
| title_auth | Modeling and estimating income data in the presence of distinctive zero and heaped responses |
| title_exact_search | Modeling and estimating income data in the presence of distinctive zero and heaped responses |
| title_full | Modeling and estimating income data in the presence of distinctive zero and heaped responses vorgelegt von Ariane Würbach, Magistra Artium |
| title_fullStr | Modeling and estimating income data in the presence of distinctive zero and heaped responses vorgelegt von Ariane Würbach, Magistra Artium |
| title_full_unstemmed | Modeling and estimating income data in the presence of distinctive zero and heaped responses vorgelegt von Ariane Würbach, Magistra Artium |
| title_short | Modeling and estimating income data in the presence of distinctive zero and heaped responses |
| title_sort | modeling and estimating income data in the presence of distinctive zero and heaped responses |
| topic | Häufigkeitsverteilung (DE-588)4158707-8 gnd Antwortverhalten (DE-588)4122814-5 gnd Einkommensverteilung (DE-588)4013898-7 gnd |
| topic_facet | Häufigkeitsverteilung Antwortverhalten Einkommensverteilung Hochschulschrift |
| url | https://fis.uni-bamberg.de/handle/uniba/41034 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029343732&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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