Download Unbalanced Panel Data … and Stata - Pdx

Unbalanced Panel Data
… and Stata
Kuan-Pin Lin
Portland State University and
WISE, Xiamen University
Panel Data Anslysis
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Panel Data Definition
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Unbalanced Panel
yit , xit (t  1, 2,..., Ti ; i  1,..., N )
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Balanced Panel: Ti  T , i
Short Panel: T  , N  
Long Panel: T  , N  
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Panel Data Models
Fixed Effects Model
 Random Effects Model
yit  xit' β  ui  vt  eit
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i  1, 2,..., N ; t  1, 2,..., Ti
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y i  Xi β  ui iTi  vTi  ei
Panel Data Analysis
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Benefits
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More Data Variability
Less Colinearity among Variables
Control for Unobserved Heterogeneity
yit  xit   ui  vt  eit
Limitations
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Time Serial and Cross Sectional Correlation
Missing Data
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Randomly and Non-randomly
Sample Selection Bias
Unbalanced Panel Data
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Incomplete panels are more likely to be the
norm in typical economic empirical studies
Problems with non-response and
measurement errors
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Gaps in Time Series
Holes in Cross Sections
Panel Data Analysis Using Stata
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Declare panel data and variables
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Panel data analysis: xt commands
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xtset
xtdes
xtsum
xtdata
xtline
Panel data regression
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xtreg
Returns to Schooling
Koops and Tobias (2004)
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Study the relationship between wages and education,
ability, and family characteristics.
Data is available in two parts. The first file contains the
panel of 17,919 observations on the Person ID and 4
time-varying variables. The second file contains time
invariant variables for the individual or the 2,178
households. See the article for details on the empirical
model and data construction (data achieve and demo
program 1 2).
Data source: U.S. National Longitudinal Survey of Youth
(NLSY), U.S. Dept. of Labor, Bureau of Labor Statistics.
Returns to Schooling
Koops and Tobias (2004)
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Part 1:
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Column 1 = Person id (ranging from 1 to 2178),
Column 2 = Education,
Column 3 = Log of hourly wage,
Column 4 = Potential experience,
Column 5 = Time trend.
Part 2:
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Column 1 = Time invariant ability,
Column 2 = Mother's education,
Column 3 = Father's education,
Column 4 = Dummy variable for residence in a broken home,
Column 5 = Number of siblings.
Housing Prices in China
Wang (2011)
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Study the effects on housing consumption and prices
from the privatization of state-owned housing reform
began in 1994 (data file and demo program 1 2)
Based on standard regression analysis, the removal of
price distortions allowed households to increase their
consumption of housing and led to an increase in
equilibrium housing prices.
Using CHNS, data is obtained for 31677 individuals from
about 2900 households in counties and cities of 9
provinces over 6 years (1989, 1991, 1993,1997, 2000,
2004).
References
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B.H. Baltagi, and S.H. Song, “Unbalanced Panel Data: A
Survey,” Statistical Papers 47, 493-523, 2006.
B.H. Baltagi, Chapter 9: Unbalanced Panel Data Models,
Econometric Analysis of Panel Data, 4th ed., John Wiley,
New York, 2008.
G. Koops, and J.L. Tobias, “Learning About
Heterogeneity in Returns to Schooling”, Journal of
Applied Econometrics 19, 827-849, 2004.
S-Y. Wang, “State Misallocation and Housing Prices:
Theory and Evidence from China”, American Economic
Review 101, 2081-2107, 2011.