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Panel Data Analysis – Advantages and Challenges Cheng Hsiao. Introduction. Year SSCI 1986 29 2003 580 2004 687 2005 773. Three factors contributing to the phenomenon growth (i) Data availability
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(i) Data availability
(ii) Greater capacity for modeling the complexity of human behavior
(iii) Challenging methodology
National Longitudinal Surveys of Labor Market Experience (NLS)
Michigan Panel Study of Income Dynamics (PSID)
The European Community Household Panel (ECHP)
Primary School Deworming Project (PDSP)
Township & Village Enterprises Survey
Financial Institutions Survey (1984-1990)
Household Demographic Survey
n x 1
n x k
k x 1
(a) Constructing and testing more complicated
- Homogenous vs Heterogenous population
- Program Evaluation
Treatment Effect =
Average Treatment Effect =
Confounding treatment effect with differences
in covariates between control group and
(b) Controlling the impact of omitted variables
(d) Generating more accurate predictions for individual outcomes (exchangeability)
(e) Providing micro foundation for aggregate data analysis
“representative agent” heterogeneity
(a) Time-series inference
(c) Dynamic sample selection models
Panel data also raises the issue of how best to model unobserved heterogeneity
Standard statistical procedures are developed based on the assumption that y conditional on x is randomly distributed with a common mean
One way to restore homogeneity is to add additional conditional variables, say, , ,… so .
(a) A model is a simplification of reality, not a mimic of reality. Multicollinearity, shortages of degree of freedom, etc. may confuse the fundamental relationship between and .
(b) , ,… may not be observable.
Meaningful inference on can be made only if we assume certain structure on .
- structural parameters
- incidental parameters (increase with N)
- individual-specific effects represent the effects of those variables that vary across individuals but stay constant over time, at least in the short-time span, e.g. ability, socio-economic background variables, marginal utility of initial wealth, etc.
- fixed constant, Fixed Effects Model (FE)
- random variable, Random Effects Model (RE)
The power of panel data to isolate the effects of specific actions, treatments or more general policies depends on the compatibility of the assumptions of statistical tools with the data generating process
Factors to consider:
(3) Compatibility between assumptions and data generating process