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Students in both social and natural sciences often seek regression methods to explain the frequency of events, such as visits to a doctor, auto accidents, or new patents awarded. This book provides the most comprehensive and up-to-date account of models and methods to interpret such data. The authors have conducted research in the field for more than twenty-five years. In this book, they combine theory and practice to make sophisticated methods of analysis accessible to researchers and practitioners working with widely different types of data and software in areas such as applied statistics, econometrics, marketing, operations research, actuarial studies, demography, biostatistics, and quantitative social sciences. The book may be used as a reference work on count models or by students seeking an authoritative overview. Complementary material in the form of data sets, template programs, and bibliographic resources can be accessed on the Internet through the authors' homepages. This second edition is an expanded and updated version of the first, with new empirical examples and more than one hundred new references added. The new material includes new theoretical topics, an updated and expanded treatment of cross-section models, coverage of bootstrap-based and simulation-based inference, expanded treatment of time series, multivariate and panel data, expanded treatment of endogenous regressors, coverage of quantile count regression, and a new chapter on Bayesian methods.

"This book is an introduction to regression analysis focusing on the practicalities of doing regression analysis on real life data. Contrary to other textbooks on regression, this book is based on the idea that you do not necessarily need to know much about statistics and mathematics to get a firm grip on regression and perform it to perfection. This non-technical point of departure is complimented by practical examples of real-life data analysis using statistics software such as Stata, R and SPSS. The first two parts of the book cover the basics, such as simple linear regression, multiple linear regression, how to interpret the output from statistics programs, significance testing and the key regression assumptions. The third part of the book deals with how to practically handle violations of the classical linear regression assumptions, regression modelling for categorical y-variables and instrumental variable (IV) regression. The fourth part of the book puts the various purposes of, or motivations for, regression into the wider context of writing a scholarly report and points to some extensions to related statistical techniques. This book is written primarily for those who need to do regression analysis in practice, and not only to understand how this method works in theory. The book's accessible approach is recommended for students from across the social sciences"--

Most major population surveys used by social scientists are based on complex sampling designs where sampling units have different probabilities of being selected. Although sampling weights must generally be used to derive unbiased estimates of univariate population characteristics, the decision about their use in regression analysis is more complicated. Where sampling weights are solely a function of independent variables included in the model, unweighted OLS estimates are preferred because they are unbiased, consistent, and have smaller standard errors than weighted OLS estimates. Where sampling weights are a function of the dependent variable (and thus of the error term), we recommend first attempting to respecify the model so that they are solely a function of the independent variables. If this can be accomplished, then unweighted OLS is again preferred. If the model cannot be respecified, then estimation of the model using sampling weights may be appropriate. In this case, however, the formula used by most computer programs for calculating standard errors will be incorrect. We recommend using the White heteroskedastic consistent estimator for the standard errors.

1. Introduction -- 2. Identifying and coding meta-analysis data -- 3. Summarizing meta-analysis data -- 4. Publication bias and its discontents -- 5. Explaining economics research -- 6. Economic theory and meta-regression analysis -- 7. Further topics in meta-regression analysis -- 8. Summary and conclusions.

"The purpose of this book is to introduce novice researchers to the tools of meta-analysis and meta-regression analysis and to summarize the state of the art for existing practitioners. Meta-regression analysis addresses the rising 'Tower of Babel' that current economics and business research has become. Meta-analysis is the statistical analysis of previously published, or reported, research findings on a given hypothesis, empirical effect, phenomenon, or policy intervention. It is a systematic review of all the relevant scientific knowledge on a specific subject and is an essential part of the evidence-based practice movement in medicine, education and the social sciences. However, research in economics and business is often fundamentally different from what is found in the sciences and thereby requires different methods for its synthesis--meta-regression analysis. This book develops, summarizes, and applies these meta-analytic methods."--Publisher description

The general minimax estimator of the linear regression model is applicable when the whole parameter vector is restricted to an ellipsoid. In many applications, however, it is more realistic to assume that only a part of the parameter set is constrained. For this case an alternative minimax approach is developed.