Presentation on theme: "Unbalanced Panel Data … and Stata Kuan-Pin Lin Portland State University and WISE, Xiamen University."— Presentation transcript:
Unbalanced Panel Data … and Stata Kuan-Pin Lin Portland State University and WISE, Xiamen University
Panel Data Anslysis Panel Data Definition Unbalanced Panel Balanced Panel: Short Panel: Long Panel: Panel Data Models Fixed Effects Model Random Effects Model
Panel Data Analysis Benefits More Data Variability Less Colinearity among Variables Control for Unobserved Heterogeneity Limitations Time Serial and Cross Sectional Correlation Missing Data Randomly and Non-randomly Sample Selection Bias
Unbalanced Panel Data Incomplete panels are more likely to be the norm in typical economic empirical studies Problems with non-response and measurement errors Gaps in Time Series Holes in Cross Sections
Panel Data Analysis Using Stata Declare panel data and variables xtset Panel data analysis: xt commands xtdes xtsum xtdata xtline Panel data regression xtreg
Returns to Schooling Koops and Tobias (2004) 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 achieve12 Data source: U.S. National Longitudinal Survey of Youth (NLSY), U.S. Dept. of Labor, Bureau of Labor Statistics.NLSY
Returns to Schooling Koops and Tobias (2004) Part 1: Part 1 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: Part 2 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) 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)data file12 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).CHNS
References 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.