Premium
Assessing Sampling Error in Pseudo‐Panel Models
Author(s) -
Khan Rumman
Publication year - 2021
Publication title -
oxford bulletin of economics and statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.131
H-Index - 73
eISSN - 1468-0084
pISSN - 0305-9049
DOI - 10.1111/obes.12416
Subject(s) - sampling (signal processing) , statistics , sampling error , monte carlo method , metric (unit) , sample size determination , sampling bias , cohort , computer science , econometrics , observational error , mathematics , operations management , filter (signal processing) , economics , computer vision
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.