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Estimation of Spatial Panels provides some recent developments on the specification and estimation of spatial panel models.
The Handbook is written for academics, researchers, practitioners and advanced graduate students. It has been designed to be read by those new or starting out in the field of spatial analysis as well as by those who are already familiar with the field. The chapters have been written in such a way that readers who are new to the field will gain important overview and insight. At the same time, those readers who are already practitioners in the field will gain through the advanced and/or updated tools and new materials and state-of-the-art developments included. This volume provides an accounting of the diversity of current and emergent approaches, not available elsewhere despite the many excellent journals and te- books that exist. Most of the chapters are original, some few are reprints from the Journal of Geographical Systems, Geographical Analysis, The Review of Regional Studies and Letters of Spatial and Resource Sciences. We let our contributors - velop, from their particular perspective and insights, their own strategies for m- ping the part of terrain for which they were responsible. As the chapters were submitted, we became the first consumers of the project we had initiated. We gained from depth, breadth and distinctiveness of our contributors’ insights and, in particular, the presence of links between them.
This work examines theoretical issues, as well as practical developments in statistical inference related to econometric models and analysis. This work offers discussions on such areas as the function of statistics in aggregation, income inequality, poverty, health, spatial econometrics, panel and survey data, bootstrapping and time series.
The Oxford Handbook of Panel Data examines new developments in the theory and applications of panel data. It includes basic topics like non-stationary panels, co-integration in panels, multifactor panel models, panel unit roots, measurement error in panels, incidental parameters and dynamic panels, spatial panels, nonparametric panel data, random coefficients, treatment effects, sample selection, count panel data, limited dependent variable panel models, unbalanced panel models with interactive effects and influential observations in panel data. Contributors to the Handbook explore applications of panel data to a wide range of topics in economics, including health, labor, marketing, trade, productivity, and macro applications in panels. This Handbook is an informative and comprehensive guide for both those who are relatively new to the field and for those wishing to extend their knowledge to the frontier. It is a trusted and definitive source on panel data, having been edited by Professor Badi Baltagi-widely recognized as one of the foremost econometricians in the area of panel data econometrics. Professor Baltagi has successfully recruited an all-star cast of experts for each of the well-chosen topics in the Handbook.
This book provides an overview of three generations of spatial econometric models: models based on cross-sectional data, static models based on spatial panels and dynamic spatial panel data models. The book not only presents different model specifications and their corresponding estimators, but also critically discusses the purposes for which these models can be used and how their results should be interpreted.
My dissertation research addresses issues in spatial panel data models, which study the interactions of economic units across space and time. Individuals interact with their neighbors and the outcomes are interdependent. The strength of the interaction depends on the distance between the individuals, which can be based on geography or constructed from economic theory. Accounting for spatial interactions allows one to quantify both the direct effect of a variable and its indirect effect through impacting neighbors. However, two issues often arise. First, spatial dependence can be alternatively generated from common unobserved factors (e.g. economy-wide shocks) where neighbors have similar responses. Second, the distance between economic units can be endogenous, and this will in fact be the case if the distance is constructed from variables that correlate with disturbances in the outcomes. The first chapter studies the estimation of a dynamic spatial panel data model with interactive individual and time effects with large n and T. The model has a rich spatial structure including contemporaneous spatial interaction and spatial heterogeneity. Dynamic features include individual time lag and spatial diffusion. In a standard two way fixed effects panel regression model, the unobservables contain an individual specific but time invariant component, and a component that is time variant but common across individuals. We generalize this model by allowing the interaction between time effects and individual effects. This chapter provides a tool for empirical researchers to guard against attributing correlated responses to common time effects as spatial effects. The interactive effects are treated as parameters, so as to allow correlations between the interactive effects and the regressors. We consider a quasi-maximum likelihood estimation and show estimator consistency and characterize its asymptotic distribution. The Monte Carlo experiment shows that the estimator performs well and the proposed bias correction is effective. The second chapter proposes a unified approach to model endogenous spatial dependences while accounting for common factors. The spatial weights matrices are constructed from variables that may correlate with the disturbances in the outcomes. We make minimal assumptions on the distributions of the factors and follow a fixed effects approach. We provide conditions under which the quasi-maximum likelihood estimator is consistent and asymptotically normal, under the asymptotics where both the cross section and time dimensions become large. The limiting distribution is normal but may not be centered for the estimates of the spatial interaction coefficient and the variances. An analytical bias correction is proposed to improve the inference. The Monte Carlo simulations demonstrate good finite sample properties of the bias corrected estimator. We illustrate the empirical relevance of the theory by applying the method to analyze the effect of house price dynamics on reverse mortgage origination rates.
‘Econometric Analysis of Panel Data’ has become established as the leading textbook for postgraduate courses in panel data. This book is intended as a companion to the main text. The prerequisites include a good background in mathematical statistics and econometrics. The companion guide will add value to the existing textbooks on panel data by solving exercises in a logical and pedagogical manner, helping the reader understand, learn and teach panel data. These exercises are based upon those in Baltagi (2008) and are complementary to that text even though they are stand alone material and the reader can learn the basic material as they go through these exercises. The exercises in this book start by providing some background material on partitioned regressions and the Frisch-Waugh-Lovell theorem, showing the reader some applications of this material that are useful in practice. Then it goes through the basic material on fixed and random effects models in a one-way and two-way error components models, following the same outline as in Baltagi (2008). The book also provides some empirical illustrations and examples using Stata and EViews that the reader can replicate. The data sets are available on the Wiley web site (www.wileyeurope.com/college/baltagi).
This dissertation is composed of three papers about the theories and application of spatial dynamic panel data model with fixed effects. The first paper investigates the asymptotic properties of quasi-maximum likelihood estimators for spatial dynamic panel data with fixed effects when both the number of individuals n and the number of time periods T are large. We consider the case where T is asymptotically large relative to n, the case where T is asymptotically proportional to n, and the case where n is asymptotically large relative to T. In the case where T is asymptotically large relative to n, the estimators are nT consistent and asymptotically normal, with the limit distribution centered around 0. When n is asymptotically proportional to T, the estimators are nT consistent and asymptotically normal, but the limit distribution is not centered around 0; and when n is large relative to T, the estimators are consistent with rate T, and have a degenerate limit distribution. We also propose a bias correction for our estimators. We show that when T grows faster than n1/3, the correction will asymptotically eliminate the bias and yield a centered confidence interval. The second paper covers a nonstationary case where there are units roots in the data generating process. When not all the roots in the DGP are unity, the estimators rate of convergence will be the same as the stationary case, and the estimators can be asymptotically normal. But for the estimators' asymptotic variance matrix, it will be driven by the nonstationary component into a singular matrix. Consequently, a linear combination of the spatial and dynamic effects can converge with a higher rate. We also propose a bias correction for our estimators. We show that when T grows faster than n 1/3, the correction will asymptotically eliminate the bias and yield a centered confidence interval. In the third paper, a spatial dynamic panel data approach is proposed to study growth convergence in the U.S. economy. In neoclassical model, countries are assumed to be independent from each other, which does not hold in the real world. We introduce technological spillovers and factor mobility into the neoclassical framework, showing that the convergence rate is higher and there is spatial correlation. Exploiting annual data on personal state income spanning period 1961-2000 for the 48 contiguous states, we obtain empirical results consistent with the model prediction.
This new edition of this established textbook reflects the rapid developments in the field covering the vast research that has been conducted on panel data since its initial publication. The book is packed with the most recent empirical examples from panel data literature and includes new data sets. The use of the standard software packages in the field i.e. STATA, LIMDEP, TSP & SAS are illustrated with new examples. The text has also been fully updated with new material on: non-stationary models, unit roots in panels and cointegration, prediction in panels, serial correlation, heteroskedasticity, and new results on GMM in dynamic panel data models. There is also website providing supplementary material for lecturers.