RT workingPaper
T1 Testing for sample selection in pseudo panels : theory and Monte Carlo
A1 Mora, Jhon James
A1 Muro Romero, Juan de Dios
K1 Repeated Cross-section Models
K1 Pseudo Panels
K1 Selectivity Bias Testing
K1 Discrete Analysis with Grouped Data
K1 Monte Carlo Methods
K1 Método de Monte Carlo
K1 Análisis discreto
K1 Prueba de selectividad diagonal
K1 Modelos repetitivos de sección transversal
K1 C23
K1 C52
K1 CIENCIAS ECONÓMICAS
K1 Estadística
K1 ECONOMICS
K1 Statistics
AB Sample selection bias is commonly used in economic models based on micro data. Despite the continuous generalization of panel data surveys, most countries still collect microeconomic information on the behavior of economic agents by means of repeated independent and representative cross-sections. This paper discusses a simple testing procedure for sample selection bias in pseudo panels. In the context of conditional mean independence panel data models we describe a pseudo panel model in which under convenient expansion of the original specification with a selectivity bias correction term the method allows us to use a Wald test of H0: as a test of the null hypothesis of absence of sample selection bias. We show that the proposed selection bias correction term is proportional to Inverse Mills ratio with an argument equal to the normit of a consistent estimation of the observed proportion of individuals in each cohort. This finding can be considered a cohort counterpart of Heckmans selectivity bias correction for the individual case and generalizes to some extent previous existing results in the empirical labour literature. Monte Carlo analysis shows the test does not reject the null for fixed T at a 5% significance level in finite samples and increases its power when utilizing cohort size corrections as suggested by Deaton (1985). As a side effect, our method enables us to make a consistent estimation of the pseudo panel parameters under rejection of the null
PB Universidad de ICESI. Departamento de Economía
YR 2007
FD 2007
LK http://hdl.handle.net/10017/663
UL http://hdl.handle.net/10017/663
LA eng
DS MINDS@UW
RD 10-ago-2022