Quality and quantity of social science data is continually improving, from large publicuse survey microdata to private industry data. This wealth of data allows researchers to ask more complex questions about interdependencies of social and economic processes and behavior. This dissertation presents methods for models that address interdisciplinary research questions about the association structure of multiple outcomes of similar or disparate types, e.g. count and duration outcomes. The proposed models and methods address associations of multiple outcomes through correlated unobserved subject-specific effects. Chapter 2 presents a semiparametric method for estimating the marginal response and association parameters in a random effects multivariate longitudinal count model. In the context of the generalized estimating equations (GEE) framework, we use a specific form of the covariance matrix of the response vector based on a model that induces dependence over time and outcomes using random effects. This moment based method is robust to distributional misspecification and reduces the computational burden associated with a high-dimensional joint distribution by avoiding parametric assumptions on the response and unobserved effects. Through a simulation study we compare finite sample robustness properties of this semiparametric method with a pseudo-likelihood approach that imposes distributional assumptions. Both of these methods are then used to analyze a dataset of insurance claim counts for three types of coverage over time. The economic significance of these results is presented in Chapter 3. Chapter 4 presents a Gaussian variational approximation (GVA) approach for estimation of a joint multivariate longitudinal count and multivariate duration random effects model. GVA proposes an approximate posterior distribution of the random effects to obtain a closed form lower bound of the marginal likelihood. GVA estimators are obtained by maximizing the variational lower bound, which coincides with minimizing the Kullback-Leibler distance between the random effects posterior distribution and the assumed approximate posterior distribution. This approach circumvents the computationally complex, high-dimensional integral associated with the marginal distribution of a joint longitudinal and duration model. Through a simulation study we compare finite sample properties of the variational approximation approach with comparable univariate and multivariate two-stage plug-in approaches. These methods are then used to analyze a dataset of insurance claim counts and policy duration for three types of coverage over time.

The application of time series techniques in economics has become increasingly important, both for forecasting purposes and in the empirical analysis of time series in general. In this book, Terence Mills not only brings together recent research at the frontiers of the subject, but also analyses the areas of most importance to applied economics. It is an up-to-date text which extends the basic techniques of analysis to cover the development of methods that can be used to analyse a wide range of economic problems. The book analyses three basic areas of time series analysis: univariate models, multivariate models, and non-linear models. In each case the basic theory is outlined and then extended to cover recent developments. Particular emphasis is placed on applications of the theory to important areas of applied economics and on the computer software and programs needed to implement the techniques. This book clearly distinguishes itself from its competitors by emphasising the techniques of time series modelling rather than technical aspects such as estimation, and by the breadth of the models considered. It features many detailed real-world examples using a wide range of actual time series. It will be useful to econometricians and specialists in forecasting and finance and accessible to most practitioners in economics and the allied professions.

This volume presents a collection of chapters focused on the study of multivariate change. As people develop and change, multivariate measurement of that change and analysis of those measures can illuminate the regularities in the trajectories of individual development, as well as time-dependent changes in population averages. As longitudinal data have recently become much more prevalent in psychology and the social sciences, models of change have become increasingly important. This collection focuses on methodological, statistical, and modeling aspects of multivariate change and applications of longitudinal models to the study of psychological processes. The volume is divided into three major sections: Extension of latent change models, Measurement and testing issues in longitudinal modeling, and Novel applications of multivariate longitudinal methodology. It is intended for advanced students and researchers interested in learning about state-of-the-art techniques for longitudinal data analysis, as well as understanding the history and development of such techniques.

Methods and Applications of Longitudinal Data Analysis describes methods for the analysis of longitudinal data in the medical, biological and behavioral sciences. It introduces basic concepts and functions including a variety of regression models, and their practical applications across many areas of research. Statistical procedures featured within the text include: descriptive methods for delineating trends over time linear mixed regression models with both fixed and random effects covariance pattern models on correlated errors generalized estimating equations nonlinear regression models for categorical repeated measurements techniques for analyzing longitudinal data with non-ignorable missing observations Emphasis is given to applications of these methods, using substantial empirical illustrations, designed to help users of statistics better analyze and understand longitudinal data. Methods and Applications of Longitudinal Data Analysis equips both graduate students and professionals to confidently apply longitudinal data analysis to their particular discipline. It also provides a valuable reference source for applied statisticians, demographers and other quantitative methodologists. From novice to professional: this book starts with the introduction of basic models and ends with the description of some of the most advanced models in longitudinal data analysis Enables students to select the correct statistical methods to apply to their longitudinal data and avoid the pitfalls associated with incorrect selection Identifies the limitations of classical repeated measures models and describes newly developed techniques, along with real-world examples.

An introduction to foundations and applications for quantitatively oriented graduate social-science students and individual researchers.

This text describes regression-based approaches to analyzing longitudinal and repeated measures data. It emphasizes statistical models, discusses the relationships between different approaches, and uses real data to illustrate practical applications. It uses commercially available software when it exists and illustrates the program code and output. The data appendix provides many real data sets-beyond those used for the examples-which can serve as the basis for exercises.

An accessible guide to the multivariate time series tools used in numerous real-world applications Multivariate Time Series Analysis: With R and Financial Applications is the much anticipated sequel coming from one of the most influential and prominent experts on the topic of time series. Through a fundamental balance of theory and methodology, the book supplies readers with a comprehensible approach to financial econometric models and their applications to real-world empirical research. Differing from the traditional approach to multivariate time series, the book focuses on reader comprehension by emphasizing structural specification, which results in simplified parsimonious VAR MA modeling. Multivariate Time Series Analysis: With R and Financial Applications utilizes the freely available R software package to explore complex data and illustrate related computation and analyses. Featuring the techniques and methodology of multivariate linear time series, stationary VAR models, VAR MA time series and models, unitroot process, factor models, and factor-augmented VAR models, the book includes: • Over 300 examples and exercises to reinforce the presented content • User-friendly R subroutines and research presented throughout to demonstrate modern applications • Numerous datasets and subroutines to provide readers with a deeper understanding of the material Multivariate Time Series Analysis is an ideal textbook for graduate-level courses on time series and quantitative finance and upper-undergraduate level statistics courses in time series. The book is also an indispensable reference for researchers and practitioners in business, finance, and econometrics.