Abstract: PANEL DATA MODELS have a long history in econometrics and have become increasingly popular in empirical economics over the past two decades. Panel data expand our opportunities to study more complex economic relationships by, for example, allowing for individual heterogeneity and dynamic feedback. These goals are often achieved by including in the model individual specific effects and lagged dependent variables. These two features of the dynamic panel data model, however, often create difficulties in estimation. Although much progress has been made in the linear panel data model (see, among others, Hsiao (1986), Baltagi (1995), and Arellano and Honore (2001) for review), our knowledge of general nonlinear dynamic panel data models is very limited. In most nonlinear models, strict exogeneity of the explanatory variables is still the key assumption. The main difficulty is that with nonlinearity, it is not obvious how to "difference away" the individual specific effects and how to use instrumental variable type techniques.2 Despite these difficulties, some developments have been made on estimating certain nonlinear dynamic models using the "fixed-effects" approach, for example, the censored regression models (Honore (1993), Honore and Hu (2001)), the sample selection models (Kyriazidou (2001)), the discrete choice models (Honor6 and Kyriazidou (2000)), and the models with multiplicative individual effects (Chamberlain (1992), Wooldridge (1997)).

Abstract: Introduction to Panel Data Issues Methods-of-Moments for Panel Linear Models Topics for Panel Linear Models Panel Data Estimators for Binary Responses Panel Data Estimators for Limited Responses Panel Data Sample Selection Models

Abstract: This work concerns the study of poverty dynamics and the analysis of the influencing socio-demographic factors. A fuzzy and multidimensional approach has been chosen in order to define two different poverty measures. A panel regression model has been estimated and particular attention has been paid to the treatment of the unobservable heterogeneity among longitudinal units. The specified model combines autoregression with variance components. The empirical analysis has been conducted using the data set of the British Household Panel Survey (BHPS) from 1991 to 1997.

Abstract: Panel data are a very valuable resource for finding empirical solutions to political science puzzles Yet numerous published studies in political science that use panel data to estimate models with dynamics have failed to take into account important estimation issues, which calls into question the inferences we can make from these analyses The failure to account explicitly for unobserved individual effects in dynamic panel data induces bias and inconsistency in cross-sectional estimators The purpose of this paper is to review dynamic panel data estimators that eliminate these problems I first show how the problems with cross-sectional estimators arise in dynamic models for panel data I then show how to correct for these problems using generalized method of moments estimators Finally, I demonstrate the usefulness of these methods with replications of analyses in the debate over the dynamics of party identification

Abstract: This article introduces panel data models briefly. First, advantages of panel data are introduced; secondly, panel Data Model Test Method is given, which presents the characteristic of panel Data; thirdly, estimation method of panel data is summarized; besides, a number of resent development of Panel Data Models is provided.