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Panel Data Analysis – Advantages and Challenges Cheng Hsiao

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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Panel Data Analysis – Advantages and Challenges Cheng Hsiao

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  1. Panel Data Analysis – Advantages and ChallengesCheng Hsiao

  2. Introduction Year SSCI 1986 29 2003 580 2004 687 2005 773

  3. Three factors contributing to the phenomenon growth (i) Data availability (ii) Greater capacity for modeling the complexity of human behavior (iii) Challenging methodology

  4. Data Availability US: National Longitudinal Surveys of Labor Market Experience (NLS) Michigan Panel Study of Income Dynamics (PSID) Eurostat: The European Community Household Panel (ECHP) Kenya: Primary School Deworming Project (PDSP) China: Township & Village Enterprises Survey Financial Institutions Survey (1984-1990) Taiwan: Household Demographic Survey

  5. Advantages • Cross-Sectional Data may reflect inter-individual differences • Time Series data may suffer from multicollinearity and shortages of degree of freedom • Panel data, by blending inter-individual indifference with intra-individual dynamics, can allow a researcher the possibility to specify more complicated behavioral hypotheses than a single cross-sectional data or time series data

  6. (i) More degree of freedom, more sample variability, less multicollinearity n x 1 n x k k x 1

  7. (ii) Greater capacity for capturing the complexity of human behavior (a) Constructing and testing more complicated behavioral hypotheses - Homogenous vs Heterogenous population Ben-Porath (1973) - Program Evaluation Difference-in-Difference method

  8. treatment if control if Treatment Effect = Average Treatment Effect = Data Confounding treatment effect with differences in covariates between control group and treatment group

  9. Bias due to selection on unobservables

  10. Difference-in-Difference method (b) Controlling the impact of omitted variables - unobservable

  11. (c) Uncovering dynamic relationships multicollinearity (d) Generating more accurate predictions for individual outcomes (exchangeability) (e) Providing micro foundation for aggregate data analysis “representative agent” heterogeneity

  12. (ii) Simplifying Statistical Inference and Computation (a) Time-series inference if if if

  13. (b) Measurement errors (c) Dynamic sample selection models

  14. Methodology Challenges 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

  15. Panel data, by its nature, focus on individual outcomes. Factors affecting individual outcomes are too numerous. One way to restore homogeneity is to add additional conditional variables, say, , ,… so . However (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.

  16. Another way is to let the parameters characterizing the conditional density of given to vary across i and/or over t, . Meaningful inference on can be made only if we assume certain structure on .

  17. Let - 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)

  18. Concluding Remarks 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: (1) Advantages (2) Limitations (3) Compatibility between assumptions and data generating process (4) Efficiency

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