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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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Presentation Transcript
introduction
Introduction

Year SSCI

1986 29

2003 580

2004 687

2005 773

slide3
Three factors contributing to the phenomenon growth

(i) Data availability

(ii) Greater capacity for modeling the complexity of human behavior

(iii) Challenging methodology

slide4
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

advantages
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
slide10
(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

slide11

treatment

if

control

if

Treatment Effect =

Average Treatment Effect =

Data

Confounding treatment effect with differences

in covariates between control group and

treatment group

slide13
Difference-in-Difference method

(b) Controlling the impact of omitted variables

- unobservable

slide14
(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

slide16
(ii) Simplifying Statistical Inference and Computation

(a) Time-series inference

if

if

if

slide17
(b) Measurement errors

(c) Dynamic sample selection models

methodology challenges
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

slide19
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.

slide20
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 .

slide21
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)

concluding remarks
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