Introduction To The Mathematical And Statistical Foundations Of Econometrics PDF Download
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Author | : Herman J. Bierens |
Publisher | : Cambridge University Press |
Total Pages | : 356 |
Release | : 2004-12-20 |
Genre | : Business & Economics |
ISBN | : 9780521542241 |
Download Introduction to the Mathematical and Statistical Foundations of Econometrics Book in PDF, ePub and Kindle
This book is intended for use in a rigorous introductory PhD level course in econometrics.
Author | : Herman J. Bierens |
Publisher | : |
Total Pages | : |
Release | : 2003 |
Genre | : Econometria |
ISBN | : |
Download Introduction to the mathematical and statistical foundations of econometrics Book in PDF, ePub and Kindle
Author | : Dale J. Poirier |
Publisher | : MIT Press |
Total Pages | : 744 |
Release | : 1995 |
Genre | : Business & Economics |
ISBN | : 9780262161497 |
Download Intermediate Statistics and Econometrics Book in PDF, ePub and Kindle
The standard introductory texts to mathematical statistics leave the Bayesian approach to be taught later in advanced topics courses-giving students the impression that Bayesian statistics provide but a few techniques appropriate in only special circumstances. Nothing could be further from the truth, argues Dale Poirier, who has developed a course for teaching comparatively both the classical and the Bayesian approaches to econometrics. Poirier's text provides a thoroughly modern, self-contained, comprehensive, and accessible treatment of the probability and statistical foundations of econometrics with special emphasis on the linear regression model. Written primarily for advanced undergraduate and graduate students who are pursuing research careers in economics, Intermediate Statistics and Econometrics offers a broad perspective, bringing together a great deal of diverse material. Its comparative approach, emphasis on regression and prediction, and numerous exercises and references provide a solid foundation for subsequent courses in econometrics and will prove a valuable resource to many nonspecialists who want to update their quantitative skills. The introduction closes with an example of a real-world data set-the Challengerspace shuttle disaster-that motivates much of the text's theoretical discussion. The ten chapters that follow cover basic concepts, special distributions, distributions of functions of random variables, sampling theory, estimation, hypothesis testing, prediction, and the linear regression model. Appendixes contain a review of matrix algebra, computation, and statistical tables.
Author | : Aris Spanos |
Publisher | : Cambridge University Press |
Total Pages | : 722 |
Release | : 1986-10-30 |
Genre | : Business & Economics |
ISBN | : 9780521269124 |
Download Statistical Foundations of Econometric Modelling Book in PDF, ePub and Kindle
A thorough foundation in probability theory and statistical inference provides an introduction to the underlying theory of econometrics that motivates the student at a intuitive as well as a formal level.
Author | : Asad Zaman |
Publisher | : Emerald Group Pub Limited |
Total Pages | : 570 |
Release | : 1996 |
Genre | : Business & Economics |
ISBN | : 9780127754154 |
Download Statistical Foundations for Econometric Techniques Book in PDF, ePub and Kindle
Statistical Foundations for Econometric Techniques features previously unavailable material in a textbook format for econometrics students, researchers, and practitioners. Taking strong positions for and against standard econometric techniques, the book endorses a single best technique whenever possible. In many cases, the recommended optimal technique differs substantially from current practice. Detailed discussions present many new estimation strategies superior to conventional OLS and ways to use them. Key Features * Evaluates econometric techniques and the procedures commonly used to analyze those techniques * Challenges established concepts * Introduces many techniques that are not available in other texts * Recommends against using the Durbin-Watson and Lagrange Multiplier tests in favor of tests with superior power * Provides many new types of estimation strategies superior to conventional OLS * Forms a judicious mixture of various methodological approaches * Illustrates Empirical Bayes estimators and Robust Regression techniques possessing a 50% breakdown value
Author | : Jianqing Fan |
Publisher | : CRC Press |
Total Pages | : 942 |
Release | : 2020-09-21 |
Genre | : Mathematics |
ISBN | : 0429527616 |
Download Statistical Foundations of Data Science Book in PDF, ePub and Kindle
Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications. The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on high-dimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning.
Author | : Jan Beran |
Publisher | : Springer |
Total Pages | : 309 |
Release | : 2018-03-23 |
Genre | : Mathematics |
ISBN | : 3319743805 |
Download Mathematical Foundations of Time Series Analysis Book in PDF, ePub and Kindle
This book provides a concise introduction to the mathematical foundations of time series analysis, with an emphasis on mathematical clarity. The text is reduced to the essential logical core, mostly using the symbolic language of mathematics, thus enabling readers to very quickly grasp the essential reasoning behind time series analysis. It appeals to anybody wanting to understand time series in a precise, mathematical manner. It is suitable for graduate courses in time series analysis but is equally useful as a reference work for students and researchers alike.
Author | : Aris Spanos |
Publisher | : Cambridge University Press |
Total Pages | : 787 |
Release | : 2019-09-19 |
Genre | : Business & Economics |
ISBN | : 1107185149 |
Download Probability Theory and Statistical Inference Book in PDF, ePub and Kindle
This empirical research methods course enables informed implementation of statistical procedures, giving rise to trustworthy evidence.
Author | : David F. Hendry |
Publisher | : Cambridge University Press |
Total Pages | : 582 |
Release | : 1997-02-20 |
Genre | : Business & Economics |
ISBN | : 9780521588706 |
Download The Foundations of Econometric Analysis Book in PDF, ePub and Kindle
Collection of classic papers by pioneer econometricians
Author | : Chris Brooks |
Publisher | : Cambridge University Press |
Total Pages | : 752 |
Release | : 2008-05-22 |
Genre | : Business & Economics |
ISBN | : 1139472305 |
Download Introductory Econometrics for Finance Book in PDF, ePub and Kindle
This best-selling textbook addresses the need for an introduction to econometrics specifically written for finance students. Key features: • Thoroughly revised and updated, including two new chapters on panel data and limited dependent variable models • Problem-solving approach assumes no prior knowledge of econometrics emphasising intuition rather than formulae, giving students the skills and confidence to estimate and interpret models • Detailed examples and case studies from finance show students how techniques are applied in real research • Sample instructions and output from the popular computer package EViews enable students to implement models themselves and understand how to interpret results • Gives advice on planning and executing a project in empirical finance, preparing students for using econometrics in practice • Covers important modern topics such as time-series forecasting, volatility modelling, switching models and simulation methods • Thoroughly class-tested in leading finance schools. Bundle with EViews student version 6 available. Please contact us for more details.