The volume includes eleven chapters written by twenty authors. These chapters

(i) investigate better methods of estimating dynamic panels;
(ii) develop methods for estimating and testing hypotheses for cointegrating vectors in dynamic panels;
(iii) extend the concept of serial correlation common features analysis to nonstationary panel data models;
(iv) study the local power of panel unit root test statistics;
(v) derive the asymptotic distributions of various estimators for the panel cointegrated regression model;
(vi) propose a unit root test in the presence of structural change;
(vii) develop a new limit theory for panel data that may be cross-sectionally heterogeneous;
(viii) propose stationarity tests for a heterogeneous panel data model;
(ix) derive instrumental variable estimators for a semiparametric partially linear dynamic panel data model;
(x) conduct Monte Carlo experiments to study the small sample properties of a growth convergence equation.
This collection of papers should prove useful for practitioners and researchers working with panel data.