Dealing With Econometrics
Download Dealing With Econometrics PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Dealing With Econometrics book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Dealing with Econometrics
Author: Jordi Ripollés
language: en
Publisher: Cambridge Scholars Publishing
Release Date: 2022-07-18
The book covers the basic statistical tools needed to analyse cross-sectional data in order to identify, quantify and evaluate possible socio-economic relationships. It contains both theoretical summaries and practical examples and exercises, some of which are solved using Excel or the Gretl software package. The exercises are mostly based on real-world data from Europe and Spain. The book also discusses basic methods, principles and practices of cross-sectional econometrics, considering simple and multiple regression analysis, statistical inference, the use of qualitative information in regression analysis and discrete choice models. In essence, it is a practical guide to the fundamentals of econometrics commonly taught in undergraduate courses in Business Administration, Finance and Accounting, and Economics in Europe.
Econometric Analysis of Cross Section and Panel Data, second edition
The second edition of a comprehensive state-of-the-art graduate level text on microeconometric methods, substantially revised and updated. The second edition of this acclaimed graduate text provides a unified treatment of two methods used in contemporary econometric research, cross section and data panel methods. By focusing on assumptions that can be given behavioral content, the book maintains an appropriate level of rigor while emphasizing intuitive thinking. The analysis covers both linear and nonlinear models, including models with dynamics and/or individual heterogeneity. In addition to general estimation frameworks (particular methods of moments and maximum likelihood), specific linear and nonlinear methods are covered in detail, including probit and logit models and their multivariate, Tobit models, models for count data, censored and missing data schemes, causal (or treatment) effects, and duration analysis. Econometric Analysis of Cross Section and Panel Data was the first graduate econometrics text to focus on microeconomic data structures, allowing assumptions to be separated into population and sampling assumptions. This second edition has been substantially updated and revised. Improvements include a broader class of models for missing data problems; more detailed treatment of cluster problems, an important topic for empirical researchers; expanded discussion of "generalized instrumental variables" (GIV) estimation; new coverage (based on the author's own recent research) of inverse probability weighting; a more complete framework for estimating treatment effects with panel data, and a firmly established link between econometric approaches to nonlinear panel data and the "generalized estimating equation" literature popular in statistics and other fields. New attention is given to explaining when particular econometric methods can be applied; the goal is not only to tell readers what does work, but why certain "obvious" procedures do not. The numerous included exercises, both theoretical and computer-based, allow the reader to extend methods covered in the text and discover new insights.
Principles of Econometrics
Author: Neeraj R Hatekar
language: en
Publisher: SAGE Publications
Release Date: 2010-11-10
This textbook makes learning the basic principles of econometrics easy for all undergraduate and graduate students of economics. It takes the readers step-by-step from introduction to understanding, first introducing the basic statistical tools like concepts of probability, statistical distributions, and hypothesis tests, and then going on to explain the two variable linear regression models along with certain additional tools like use of dummy variables, various data transformations amongst others. The most innovative feature of this textbook is that it familiarizes students with the role of R, which is a flexible and popular programming language. With its help, the student will be able to implement a linear regression model and deal with the associated problems with substantial confidence.