Econometric Exercises – serie

Building on the success of Abadir and Magnus' Matrix Algebra in the Econometric Exercises Series, Statistics serves as a bridge between elementary and specialized statistics. Professors Abadir, Heijmans, and Magnus freely use matrix algebra to cover intermediate to advanced material. Each chapter contains a general introduction, followed by a series of connected exercises which build up knowledge systematically. The characteristic feature of the book (and indeed the series) is that all exercises are fully solved. The authors present many new proofs of established results, along with new results, often involving shortcuts that resort to statistical conditioning arguments.

Matrix Algebra is the first volume of the Econometric Exercises Series. It contains exercises relating to course material in matrix algebra that students are expected to know while enrolled in an (advanced) undergraduate or a postgraduate course in econometrics or statistics. The book contains a comprehensive collection of exercises, all with full answers. But the book is not just a collection of exercises; in fact, it is a textbook, though one that is organized in a completely different manner than the usual textbook. The volume can be used either as a self-contained course in matrix algebra or as a supplementary text.

Building on the success of Abadir and Magnus' Matrix Algebra in the Econometric Exercises Series, Statistics serves as a bridge between elementary and specialized statistics. Professors Abadir, Heijmans, and Magnus freely use matrix algebra to cover intermediate to advanced material. Each chapter contains a general introduction, followed by a series of connected exercises which build up knowledge systematically. The characteristic feature of the book (and indeed the series) is that all exercises are fully solved. The authors present many new proofs of established results, along with new results, often involving shortcuts that resort to statistical conditioning arguments.

Bayesian Econometric Methods examines principles of Bayesian inference by posing a series of theoretical and applied questions and providing detailed solutions to those questions. This second edition adds extensive coverage of models popular in finance and macroeconomics, including state space and unobserved components models, stochastic volatility models, ARCH, GARCH, and vector autoregressive models. The authors have also added many new exercises related to Gibbs sampling and Markov Chain Monte Carlo (MCMC) methods. The text includes regression-based and hierarchical specifications, models based upon latent variable representations, and mixture and time series specifications. MCMC methods are discussed and illustrated in detail - from introductory applications to those at the current research frontier - and MATLAB® computer programs are provided on the website accompanying the text. Suitable for graduate study in economics, the text should also be of interest to students studying statistics, finance, marketing, and agricultural economics.