The Econometrics of Multi-dimensional Panels (2^nd Ed., 2nd ed. 2024)
Theory and Applications
Advanced Studies in Theoretical and Applied Econometrics Series, Vol. 54

This book presents the econometric foundations and applications of multi-dimensional panels, including modern methods of big data analysis. In light of the big data revolution and the emergence of higher dimensional panel data sets, it provides new results to synthesize existing knowledge on the field. The first, theoretical part of the volume is providing the econometric foundations to deal with these new high-dimensional panel data sets. It not only synthesizes our current knowledge, but mostly, presents new research results. The second empirical part of the book provides insight into the most relevant applications in this area. These chapters are a mixture of surveys and new results, always focusing on the econometric problems and feasible solutions.

This second extended and revised edition provides an update of all existent chapters to reflect on new developments in the area as well as several new chapters on topics such as machine learning, nonparametric models,networks, and multi-dimensional panels in health economics. The book serves as a standard reference work, a textbook for graduate students in economics, and a source of background material for professionals conducting empirical studies.

Fixed Effects Models.- When and How Much Do Fixed Effects Matter?- Random Effects Models.-  Estimation of Sparse Variance-Covariance Matrix.- Models with Endogenous Regressors.- Dynamic Models and Reciprocity.- Random Coefficients Models.- Nonparametric Models with Random Effects.- Nonparametric Models with Fixed Effects.- Multi-dimensional Panels in Quantile Regression Models.- Multi-dimensional Models for Spatial Panels.- The Econometrics of Gravity Models in International Trade.- Modelling Housing Using Multi-dimensional Panel Data.- Modelling Migration.- Multi-dimensional Panels in Health Economics with an Application on Antibiotic Consumption.- Can Machine Learning Beat Gravity in Flow Prediction?

Laszlo Matyas is a well-known Hungarian-Australian economist/econometrician. He (co)authored and (co)edited several high impact publications in econometrics, mostly in the field of panel data. Currently he is a University Professor at the Central European University (CEU – Budapest, Hungary and Vienna, Austria). Earlier, among others, he worked as Senior Lecturer at Monash University (Melbourne, Australia), was the founding Director of the Institute for Economic Analysis (Budapest, Hungary), and also served as Provost of CEU.