panel data in stata - Panel data methods for...

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Panel data methods for microeconometrics using Stata A. Colin Cameron Univ. of California - Davis Prepared for West Coast Stata Users&Group Meeting Based on A. Colin Cameron and Pravin K. Trivedi, Microeconometrics using Stata, Stata Press, forthcoming. October 25, 2007 A. Colin Cameron P Panel methods for Stata October 25, 2007 1 / 39
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1. Introduction Panel data are repeated measures on individuals ( i ) over time ( t ) . Regress y it on x it for i = 1 , ..., N and t = 1 , ..., T . Complications compared to cross-section data: 1 Inference: correct (in&ate) standard errors. This is because each additional year of data is not independent of previous years. 2 Modelling: richer models and estimation methods are possible with repeated measures. Fixed e/ects and dynamic models are examples. 3 Methodology: di/erent areas of applied statistics may apply di/erent methods to the same panel data set. A. Colin Cameron P Panel methods for Stata October 25, 2007 2 ² 39
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This talk: overview of panel data methods and xt commands for Stata 10 most commonly used by microeconometricians . Three specializations to general panel methods: 1 Short panel: data on many individual units and few time periods. Then data viewed as clustered on the individual unit. Many panel methods also apply to clustered data such as cross-section individual-level surveys clustered at the village level. 2 Causation from observational data : use repeated measures to estimate key marginal e/ects that are causative rather than mere correlation. Fixed e/ects: assume time-invariant individual-speci&c e/ects. IV: use data from other periods as instruments. 3 Dynamic models: regressors include lagged dependent variables. A. Colin Cameron P Panel methods for Stata October 25, 2007 3 ² 39
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Outline 1 Introduction 2 Linear models overview 3 Example: wages 4 Standard linear panel estimators 5 Linear panel IV estimators 6 Linear dynamic models 7 Long panels 8 Random coe¢ cient models 9 Clustered data 10 Nonlinear panel models overview 11 Nonlinear panel models estimators 12 Conclusions A. Colin Cameron P Panel methods for Stata October 25, 2007 4 / 39
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2.1 Some basic considerations 1 Regular time intervals assumed. 2 Unbalanced panel okay (xt commands handle unbalanced data). [Should then rule out selection/attrition bias]. 3 Short panel assumed, with T small and N ! . [Versus long panels, with T ! and N small or N ! .] 4 Errors are correlated. [For short panel: panel over t for given i , but not over i .] 5 Parameters may vary over individuals or time. Intercept: Individual-speci&c e±ects model (&xed or random e±ects). Slopes: Pooling and random coe¢ cients models. 6 Regressors : time-invariant, individual-invariant, or vary over both. 7 Prediction : ignored. [Not always possible even if marginal e±ects computed.] 8 Dynamic models : possible. [Usually static models are estimated.] A. Colin Cameron P Panel methods for Stata October 25, 2007 5 / 39
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2.2 Basic linear panel models Pooled model (or population-averaged) y it = α + x 0 it β + u it . (1) Two-way e/ects model allows intercept to vary over i and t y it = α i + γ t + x 0 it β + ε it . (2) Individual-speci&c e/ects model y it =
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panel data in stata - Panel data methods for...

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