High-Dimensional Econometrics and Identification

In many applications of econometrics and economics, a large proportion of the questions of interest are identification. An economist may be interested in uncovering the true signal when the data could be very noisy, such as time-series spurious regression and weak instruments problems, to name a few. In this book, High-Dimensional Econometrics and Identification, we illustrate the true signal and, hence, identification can be recovered even with noisy data in high-dimensional data, e.g., large panels. High-dimensional data in econometrics is the rule rather than the exception. One of the tools to analyze large, high-dimensional data is the panel data model.

High-Dimensional Econometrics and Identification grew out of research work on the identification and high-dimensional econometrics that we have collaborated on over the years, and it aims to provide an up-todate presentation of the issues of identification and high-dimensional econometrics, as well as insights into the use of these results in empirical studies. This book is designed for high-level graduate courses in econometrics and statistics, as well as used as a reference for researchers.
Contents: PrefaceAbout the AuthorsPanel Data Model with Stationary and Nonstationary Regressors and Error TermsPanel Time Trend Model with Stationary and Nonstationary Error TermsEstimation of Change Points in Stationary and Nonstationary Regressors and Error TermWeak Instruments in Panel Data ModelsIncidental Parameters Problem in Panel Data ModelsBibliographyIndex
Readership: Graduate and researchers in the field of econometrics and economics. Large Dimensional;Large Panel;Identification;High-Dimensional Econometrics;Econometrics; Statistics;True Signal;High-Dimensional Data;Panel Data Model;Panel Data;Panel Spurious Regressions;Autocorrelation Parameter;Dynamic Linear Panels;Incidental Parameters0Key Features:This book focuses on panel data models with both large cross-sectional dimension, n, and time-series dimension TExisting panel data textbooks, such as Baltagi (2013), Hsiao (2014) and Pesaran (2015), usually study panel data models with a large dimension n but a fixed dimension T. Different from them, we show in this book that identification can be restored in a panel data with large dimensions n and T