Mathematical Exercises in Econometric Theory and Practice: A Useful Companion Reference for Most Econometrics Books by Greene, Hamilton, Hayashi, White, and Wooldridge

This book collects many useful mathematical exercises in postgraduate econometrics. Many quantitative exercises, examples, codes, and solutions draw from the collective wisdom of econometrics books by Greene, Hamilton, Hayashi, White, and Wooldridge. The reader can benefit from these solutions with many mathematical equations and notations to become familiar with the core curriculum for econometric theory and practice. The core topics cover (non)linear least squares theory, quasi-maximum likelihood estimation, twin-step minimax estimation, financial time-series econometric theory (e.g. ARMA-GARCH, ARMA-EGARCH, and ARMA-GJR-GARCH), generalized method of moments (GMM) with exogenous instrumental variables, panel estimation with endogeneity and unobservable heterogeneity, binary response model estimation (e.g. probit and logit), multinomial response model estimation (e.g. conditional logit and Berry-Levinsohn-Pakes random-coefficients mixed logit), Tobit model estimation with corner solutions, Heckman sample selection model estimation, and so forth.

Although this book does not serve as an attempt to cover all relevant areas of postgraduate econometrics, this book encapsulates numerous hard and solid exercises that require mathematical logic to derive econometric results. The editor puts a great emphasis on hypothesis test development and conceptual econometric context for the reader to better appreciate the substantive content of postgraduate econometrics. This content spans at least a full year of twin sequential courses on econometric theory.