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This paper considers the problem of aggregation in the case of large linear dynamic panels, where each micro unit is potentially related to all other micro units, and where micro innovations are allowed to be cross sectionally dependent. Following Pesaran (2003), an optimal aggregate function is derived, and the limiting behavior of the aggregation error is investigated as N (the number of cross section units) increases. Certain distributional features of micro parameters are also identified from the aggregate function. The paper then establishes Granger's (1980) conjecture regarding the long memory properties of aggregate variables from .a very large scale dynamic, econometric model., and considers the time profiles of the effects of macro and micro shocks on the aggregate and disaggregate variables. Some of these findings are illustrated in Monte Carlo experiments, where we also study the estimation of the aggregate effects of micro and macro shocks. The paper concludes with an empirical application to consumer price inflation in Germany, France and Italy, and re-examines the extent to which "observed" inflation persistence at the aggregate level is due to aggregation and/or common unobserved factors. Our findings suggest that dynamic heterogeneity as well as persistent common factors are needed for explaining the observed persistence of the aggregate inflation.

Introduction / Badi H. Baltagi, Thomas B. Fomby, R. Carter Hill -- Testing for common cyclical features in nonstationary panel data models / Alain Hecq, Franz C. Palm, Jean-Pierre Urbain -- The local power of some unit root tests for panel data / J(c)·org Breitung -- On the estimation and inference of a cointegrated regression in panel data / Chihwa Kao, Min-Hsien Chiang -- Testing for unit roots in panels in the presence of structural change with an application to OECD unemployment / Christian J. Murray, David H. Papell -- Panel data limit theory and asymptotic analysis of a panel regression with near integrated regressors / Heikki Kauppi -- Stationarity tests in heterogeneous panels / Yong Yin, Shaowen Wu -- Instrumental variable estimation of semiparametric dynamic panel data models : Monte Carlo results on several new and existing estimators / M. Douglas Berg, Qi Li, Aman Ullah -- Small sample performance of dynamic panel data estimators in estimating the growth-convergence equation : a Monte Carlo study / Nazrul Islam -- Estimation in dynamic panel data models : improving on the performance of the standard GMM estimator / Richard Blundell, Stephen Bond, Frank Windmeijer -- Nonstationary panels, cointegration in panels and dynamic panels : a survey / Badi H. Baltagi, Chihwa Kao -- Fully modified OLS for heterogeneous cointegrated panels / Peter Pedroni. - This volume is dedicated to two recent intensive areas of research in the econometrics of panel data, namely nonstationary panels and dynamic panels. It includes a comprehensive survey of the nonstationary panel literature including panel unit root tests, spurious panel regressions and panel cointegration tests. In addition, it provides recent developments in the estimation of dynamic panel data models using generalized method of moments. The volume includes eleven chapters written by twenty authors. These chapters: investigate better methods of estimating dynamic panels; develop methods for estimating and testing hypotheses for cointegrating vectors in dynamic panels; extend the concept of serial correlation common features analysis to nonstationary panel data models; study the local power of panel unit root test statistics; derive the asymptotic distributions of various estimators for the panel cointegrated regression model; propose a unit root test in the presence of structural change; develop a new limit theory for panel data that may be cross-sectionally heterogeneous; propose stationarity tests for a heterogeneous panel data model; derive instrumental variable estimators for a semiparametric partially linear dynamic panel data model; and 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

Introduction / Badi H. Baltagi, Thomas B. Fomby, R. Carter Hill -- Testing for common cyclical features in nonstationary panel data models / Alain Hecq, Franz C. Palm, Jean-Pierre Urbain -- The local power of some unit root tests for panel data / J(c)·org Breitung -- On the estimation and inference of a cointegrated regression in panel data / Chihwa Kao, Min-Hsien Chiang -- Testing for unit roots in panels in the presence of structural change with an application to OECD unemployment / Christian J. Murray, David H. Papell -- Panel data limit theory and asymptotic analysis of a panel regression with near integrated regressors / Heikki Kauppi -- Stationarity tests in heterogeneous panels / Yong Yin, Shaowen Wu -- Instrumental variable estimation of semiparametric dynamic panel data models : Monte Carlo results on several new and existing estimators / M. Douglas Berg, Qi Li, Aman Ullah -- Small sample performance of dynamic panel data estimators in estimating the growth-convergence equation : a Monte Carlo study / Nazrul Islam -- Estimation in dynamic panel data models : improving on the performance of the standard GMM estimator / Richard Blundell, Stephen Bond, Frank Windmeijer -- Nonstationary panels, cointegration in panels and dynamic panels : a survey / Badi H. Baltagi, Chihwa Kao -- Fully modified OLS for heterogeneous cointegrated panels / Peter Pedroni

This article considers the determinants of Portuguese tourism demand for the period 2004-2013. The econometric methodology uses a panel unit root test and the dynamic panel data (GMM-system estimator). The different techniques of panel unit root (Levin, Lin and Chu; Im, Pesaran and Shin W-stat and augmented Dickey-Fuller - Fisher Chi-square) show that the variables used in this panel are stationary. The dynamic model proves that tourism demand is a dynamic process. The variables relative prices, income per capita, human capital and government spending encourage international tourism demand for Portugal. ; info:eu-repo/semantics/publishedVersion

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.

This article investigates inconsistency and invalid statistical inference that often characterize dynamic panel analysis in international political economy. These econometric concerns are tied to Nickell bias and cross-sectional dependence. First, we discuss how to avoid Nickell bias in dynamic panels. Second, we put forward factor-augmented dynamic panel regression as a means for addressing cross-sectional dependence. As a specific application, we use our methods for an analysis of the impact of terrorism on economic growth. Different terrorism variables are shown to have no influence on economic growth for five regional samples when Nickell bias and cross-dependence are taken into account. Our finding about terrorism and growth is contrary to the extant literature.

The article aims at examination of the shape of relationship between income inequality and the level of economic development measured by GDP per capita in 27 European Union countries in the period of 2004-2014. It also aims at identification of determinants of income inequality. Specifically, we test for the existence of an inverted U-shaped curve, as it is predicted by the standard Kuznets hypothesis, and J-shaped curve following the approach adopted by Deutsch and Silber (2004) and Anand and Kanbur (1993). The data come from Eurostat EU-SILC database (European Union Statistics on Income and Living Conditions), World Bank and International Monetary Fund. In the EU-27 group of countries we contradict the Kuznets hypothesis – our results provide evidence for a U-shaped, rather than the inverted U relationship. It also follows from our analysis that our data cover only the descending part of the U, that is a shape of inverted J.

This article analyzes a growing group of fixed T dynamic panel data estimators with a multifactor error structure. We use a unified notational approach to describe these estimators and discuss their properties in terms of deviations from an underlying set of basic assumptions. Furthermore, we consider the extendability of these estimators to practical situations that may frequently arise, such as their ability to accommodate unbalanced panels and common observed factors. Using a large-scale simulation exercise, we consider scenarios that remain largely unexplored in the literature, albeit being of great empirical relevance. In particular, we examine (i) the effect of the presence of weakly exogenous covariates, (ii) the effect of changing the magnitude of the correlation between the factor loadings of the dependent variable and those of the covariates, (iii) the impact of the number of moment conditions on bias and size for GMM estimators, and finally (iv) the effect of sample size. We apply each of these estimators to a crime application using a panel data set of local government authorities in New South Wales, Australia; we find that the results bear substantially different policy implications relative to those potentially derived from standard dynamic panel GMM estimators. Thus, our study may serve as a useful guide to practitioners who wish to allow for multiplicative sources of unobserved heterogeneity in their model.