PanelDataNotes-3

PanelDataNotes-3 - Econometric Analysis of Panel Data...

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Econometric Analysis of Panel Data William Greene Department of Economics Stern School of Business
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Econometric Analysis of Panel Data 3. Panel Data and Individual Effects
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Panel Data Sets * Longitudinal data – ‘short panels’ National longitudinal survey of youth (NLS) British household panel survey (BHPS) Panel Study of Income Dynamics (PSID) Cross section time series – ‘long panels’ Grunfeld’s investment data Penn world tables Financial data by firm, year – ‘huge panels’ r it  – r ft  =  β i (r mt   -  r ft )  +   ε it , i = 1,…,many; t=1,…many Exchange rate data, essentially infinite T, large N Effects:  β i β  + v i * See Baltagi, Chapter 1
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Panel Data Rotating panels: Spanish household survey Spanish income/savings study Efficiency analysis: “Efficiency measurement in rotating panel  data,” Heshmati, A,  Applied Economics , 30, 1998, pp. 919-930 Pseudo panel: Time series of (different) cross sections.  E.g., Yearly UK Family Expenditure Survey; 7,000+  different households. What can we learn from these? Hierarchical (nested) data sets: Student outcome, by  year, district, school, teacher
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Nested Panel Data Antweiler, W., Nested Random Effects…”  Journal of Econometrics, 101, 2001, 295-313 2 1 2 c,s,t 3 c,t 4 8 9 , , , Sulfide concentration(year,country,station= t,c,s) + (logGDP/km ) + log(K/L) β β β   ... +   log( Pr ) c t c s t c s s Communist Oil ice t v w + β + β + β + ε + +
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Balanced and Unbalanced Panels Distinction A notation to help with mechanics             z i,t , i = 1,…,N;  t = 1,…,T i The role of the assumption  Mathematical and notational convenience: Balanced, n=NT Unbalanced: Is the fixed T i  assumption ever necessary?  SUR  models.   Applications: See the data sets for the course. = N i i= 1 T n
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An Unbalanced Panel: RWM’s GSOEP Data on Health Care N = 7,293 Households
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Benefits of Panel Data Time and individual variation in behavior unobservable  in cross sections or aggregate time series Observable and unobservable individual heterogeneity Rich hierarchical structures More complicated models Features that cannot be modeled with only cross  section or aggregate time series data alone Dynamics in economic behavior
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Short Term Agenda for Simple Effects Models Models with individual effects Interpretation of models Computation (practice) and estimation (theory) Extensions Nonstandard panels: Rotating, Pseudo-, Nested Generalizing the regression model Alternative estimators Methods Least squares: OLS, GLS, FGLS MLE and Maximum Simulated Likelihood
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Fixed and Random Effects Unobserved individual effects in regression:  E[y it  |  x it , c i ]
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