Random Coefficient Models for Time-Series—Cross-Section Data
Monte Carlo Experiments
Bibliographic Data
| ID | 6331995 |
|---|---|
| Authors | Niels Beck (0000-0002-7533-0990, New York University), Nathaniel Beck, Jonathan Ned Katz (0000-0002-5287-3503, California Institute of Technology) |
| Year | 2007 |
| Volume | 15 |
| Issue | 2 |
| Pages | 182-195 |
| Publication date | 2007-01-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Political Analysis (JOURNAL) |
| Journal identifiers | ISSN: 1047-1987 • E-ISSN: 1476-4989 |
| Publisher | Cambridge University Press (CUP) (PUBLISHER) |
| DOI | 10.1093/pan/mpl001 |
| OpenAlex | W2160503536 |
| Language | EN |
| Citations received | 78 |
| References cited | 12 |
This article considers random coefficient models (RCMs) for time-series—cross-section data. These models allow for unit to unit variation in the model parameters. The heart of the article compares the finite sample properties of the fully pooled estimator, the unit by unit (unpooled) estimator, and the (maximum likelihood) RCM estimator. The maximum likelihood estimator RCM performs well, even where the data were generated so that the RCM would be problematic. In an appendix, we show that the most common feasible generalized least squares estimator of the RCM models is always inferior to the maximum likelihood estimator, and in smaller samples dramatically so
Bias of an estimator · Estimator · Maximum likelihood · Minimum-variance unbiased estimator · Monte Carlo method · Section (typography · Series (stratigraphy · Statistics · Trimmed estimator · Computer Science · Mathematics · Monetary Policy and Economic Impact · Spatial and Panel Data Analysis · Statistical Methods and Inference · Applied Mathematics
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| Unique citing works | 78 |
|---|---|
| Citations per year | 4,11 |
| Citation span | 2007 - 2026 (20) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 77 |