Assessing Sampling Error in Pseudo‐Panel Models
Bibliographic Data
| ID | 21513019 |
|---|---|
| Authors | Rumman Khan (0000-0003-3735-9730, School of Economics University of Bristol Bristol UK, corresponding author) |
| Year | 2021 |
| Volume | 83 |
| Issue | 3 |
| Pages | 742-769 |
| Publication date | 2021-06-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Oxford Bulletin of Economics and Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0305-9049 • E-ISSN: 1468-0084 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/obes.12416 |
| OpenAlex | W3118730983 |
| Language | EN |
| Citations received | 3 |
| References cited | 28 |
While pseudo‐panels are useful when only repeated cross‐section data are available, estimates are likely to be attenuated and suffer from sampling error if cell sizes (number of individuals grouped together in a cohort) are too few. However, there is no consensus on how large cell size needs to be, with recommendations ranging from 100 to several thousands. This is due to sampling error being affected by both cell size and three important types of variation in the cohort data (across and within cohorts and over time). We combine these into a single metric, called CAWAR, and demonstrate its relationship to sampling error using Monte Carlo simulations and an empirical application. We produce recommended values for CAWAR beyond which sampling error bias is minimal and from these one can easily calculate the required cell size
Cohort · Econometrics · Monte Carlo method · Observational error · Sample size determination · Sampling bias · Sampling error · Statistics · Computer Science · demographic modeling and climate adaptation · Health disparities and outcomes · Mathematics · Spatial and Panel Data Analysis
Panel data from time series of cross-sections
A Profitable Approach to Labor Supply and Commodity Demands over the Life-Cycle
Pseudo Panel Data Models With Cohort Interactive Effects
Improved Errors-in-Variables Estimators for Grouped Data
The Information Approach to the Aggregation of Input-Output Tables
Measuring Vulnerability to Asset-Poverty in Sub-Saharan Africa
Women’s Empowerment Across the Life Cycle and Generations
Women’s Empowerment and Prevalence of Stunted and Underweight Children in Rural India
Returns to Education in India
The Demand for Private Medical Insurance in the UK
Earnings Mobility and Measurement Error
| Unique citing works | 3 |
|---|---|
| Citations per year | 1 |
| Citation span | 2023 - 2023 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 3 |