Panel Data Models: Some Recent Developments
AbstractThis chapter focuses on two of the developments in panel data econometrics since the Handbook chapter by Chamberlain (1984). The first objective of this chapter is to provide a review of linear panel data models with predetermined variables. We discuss the implications of assuming that explanatory variables are predetermined as opposed to strictly exogenous in dynamic structural equations with unobserved heterogeneity. We compare the identification from moment conditions in each case, and the implications of alternative feedback schemes for the time series properties of the errors. We next consider autoregressive error component models under various auxiliary assumptions. There is a trade-off between robustness and efficiency since assumptions of stationary initial conditions or time series homoskedasticity can be very informative, but estimators are not robust to their violation. We also discuss the identification problems that arise in models with predetermined variables and multiple effects. Concerning inference in linear models with predetermined variables, we discuss the form of optimal instruments, and the sampling properties of GMM and LIML-analogue estimators drawing on Monte Carlo results and asymptotic approximations.
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Bibliographic InfoPaper provided by Centro de Estudios Monetarios Y Financieros- in its series Papers with number 0016.
Length: 88 pages
Date of creation: 2000
Date of revision:
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Postal: Centro de Estudios Monetarios Y Financieros. Casado del Alisal, 5-28014 Madrid, Spain.
Web page: http://www.cemfi.es/
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ECONOMETRICS ; MODELS ; TIME SERIES;
Other versions of this item:
- C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
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