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Simona Boffelli and Giovanni Urga [2018]

Financial Econometrics Using Stata

Financial Econometrics Using Stata by Simona Boffelli and Giovanni Urga provides an excellent introduction to time-series analysis and how to do it in Stata for financial economists. Aimed at researchers, graduate students, and industry practitioners, this book introduces readers to widely used methods, shows them how to perform these methods in Stata, and illustrates how to interpret the results.

Patrick Royston and Paul C. Lambert [2011]

Flexible Parametric Survival Analysis Using Stata: Beyond the Cox Model

Researchers wishing to fit regression models to survival data have long faced the difficult task of choosing between the Cox model and a parametric survival model, such as Weibull. Cox models are fit using Stata’s stcox command, and parametric models are fit using streg, which offers five parametric forms in addition to Weibull. While the Cox model makes minimal assumptions about the form of the baseline hazard function, prediction of hazards and other related functions for a given set of covariates is hindered by this lack of assumptions; the resulting estimated curves are not smooth and do not possess information about what occurs between the observed failure times. Parametric models offer nice, smooth predictions by assuming a functional form of the hazard, but often the assumed form is too structured for use with real data, especially if there exist significant changes in the shape of the hazard over time.

72 CHF / 61 CHF (eBook)
James W. Hardin and Joseph M. Hilbe [2018]

Generalized Linear Models and Extensions, Fourth Edition

Generalized linear models (GLMs) extend linear regression to models with a non-Gaussian or even discrete response. GLM theory is predicated on the exponential family of distributions—a class so rich that it includes the commonly used logit, probit, and Poisson models. Although one can fit these models in Stata by using specialized commands (for example, logit for logit models), fitting them as GLMs with Stata’s glm command offers some advantages. For example, model diagnostics may be calculated and interpreted similarly regardless of the assumed distribution.

72 CHF
Partha Deb, Edward C. Norton, Willard G. Manning [2017]

Health Econometrics Using Stata

Health Econometrics Using Stata by Partha Deb, Edward C. Norton, and Willard G. Manning provides an excellent overview of the methods used to analyze data on healthcare expenditure and use. Aimed at researchers, graduate students, and practitioners, this book introduces readers to widely used methods, shows them how to perform these methods in Stata, and illustrates how to interpret the results. Each method is discussed in the context of an example using an extract from the Medical Expenditure Panel Survey.

Michael N. Mitchell [2012]

Interpreting and Visualizing Regression Models Using Stata

Michael Mitchell’s Interpreting and Visualizing Regression Models Using Stata is a clear treatment of how to carefully present results from model-fitting in a wide variety of settings. It is a boon to anyone who has to present the tangible meaning of a complex model in a clear fashion, regardless of the audience. As an example, many experienced researchers start to squirm when asked to give a simple explanation of the practical meaning of interactions in nonlinear models such as logistic regression. The techniques presented in Mitchell's book make answering those questions easy. The overarching theme of the book is that graphs make interpreting even the most complicated models containing interaction terms, categorical variables, and other intricacies straightforward.

68 CHF / 66 CHF (eBook)
Sean Becketti [2013]

Introduction to Time Series Using Stata

Introduction to Time Series Using Stata, by Sean Becketti, provides a practical guide to working with time-series data using Stata and will appeal to a broad range of users. The many examples, concise explanations that focus on intuition, and useful tips based on the author’s decades of experience using time-series methods make the book insightful not just for academic users but also for practitioners in industry and government.

68 CHF / 67 CHF (eBook)
William Gould, Jeffrey Pitblado, and Brian Poi [2010]

Maximum Likelihood Estimation with Stata, Fourth Edition

Maximum Likelihood Estimation with Stata, Fourth Edition is the essential reference and guide for researchers in all disciplines who wish to write maximum likelihood (ML) estimators in Stata. Beyond providing comprehensive coverage of Stata’s ml command for writing ML estimators, the book presents an overview of the underpinnings of maximum likelihood and how to think about ML estimation.

62 CHF / 61 CHF (eBook)
Tom M. Palmer and Jonathan A. C. Sterne (editors) [2016]

Meta-Analysis in Stata: An Updated Collection from the Stata Journal, Second Edition

Meta-analysis allows researchers to combine results of several studies into a unified analysis that provides an overall estimate of the effect of interest and to quantify the uncertainty of that estimate. Stata has some of the best statistical tools available for doing meta-analysis. The unusual thing about these tools is that none of them are part of official Stata. They are all created by and documented by experts in the broader research community who also happen to be proficient Stata developers.

51 CHF / 54 CHF (eBook)
A. Colin Cameron and Pravin K. Trivedi [2010]

Microeconometrics Using Stata, Revised Edition

Microeconometrics Using Stata, Revised Edition, by A. Colin Cameron and Pravin K. Trivedi, is an outstanding introduction to microeconometrics and how to do microeconometric research using Stata. Aimed at students and researchers, this book covers topics left out of microeconometrics textbooks and omitted from basic introductions to Stata. Cameron and Trivedi provide the most complete and up-to-date survey of microeconometric methods available in Stata.

69 CHF / 75 CHF (eBook)
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