Software for Data Analysis

Provides statistical tools and techniques needed to understand today's financial markets The Second Edition of this critically acclaimed text provides a comprehensive and systematic introduction to financial econometric models and their applications in modeling and predicting financial time series data. This latest edition continues to emphasize empirical financial data and focuses on real-world examples. Following this approach, readers will master key aspects of financial time series, including volatility modeling, neural network applications, market microstructure and high-frequency financial data, continuous-time models and Ito's Lemma, Value at Risk, multiple returns analysis, financial factor models, and econometric modeling via computation-intensive methods. The author begins with the basic characteristics of financial time series data, setting the foundation for the three main topics: Analysis and application of univariate financial time series Return series of multiple assets Bayesian inference in finance methods This new edition is a thoroughly revised and updated text, including the addition of S-Plus® commands and illustrations. Exercises have been thoroughly updated and expanded and include the most current data, providing readers with more opportunities to put the models and methods into practice. Among the new material added to the text, readers will find: Consistent covariance estimation under heteroscedasticity and serial correlation Alternative approaches to volatility modeling Financial factor models State-space models Kalman filtering Estimation of stochastic diffusion models The tools provided in this text aid readers in developing a deeper understanding of financial markets through firsthand experience in working with financial data. This is an ideal textbook for MBA students as well as a reference for researchers and professionals in business and finance.

The first part of this book is devoted to methods seeking relevant dimensions of data. The second part is devoted to clustering methods which constitute another method, often complementary to the methods described in the first part, to synthesize and to analyze the data.

The book carefully designs its approach, starting from single-level then to multi-level models, also provides the methodologies with broad practical examples (using R) and graphical displays. It gives useful guidance for social scientist and other applied statisticians into linear regression, logistic regression, GLM, and hierarchical models.

Dyadic data is a commonly found data structure in social psychology and social relations research. The authors describe and demonstrate several statistical methods, including multilevel and structural equation modeling approaches.

Bruce Thompson shows readers how to use the latest techniques for interpreting research outcomes as well as how to make statistical decisions that result in better research.
Thompson integrates a broad array of methods involving only a single dependent variable, ranging from classical and robust location descriptive statistics, through effect sizes, and on through ANOVA, multiple regression, loglinear analysis and logistic regression. Special features include SPSS and Excel demonstrations that offer opportunities, in the book’s datasets and on Thompson’s website.

One of the best understandable econometrics books. Excellent discussions on classical linear regression model, and also explanations on other quatitative methods: survival models, Bayesian approach, panel data, neural networks, time series, etc.

The book provides clear identification of key issues in research design (i.e. measurement, sampling and causation) as well as major data collection techniques. Excellent balance between qualitative and quantitative methods. Integration of research ethics with the pertinent methodological topics.

Focusing on an analysis of models and data that arise from repeated observations of a cross-section of individuals, households or firms, this book also covers important applications within business, economics, education, political science and other social science disciplines. The author introduces the foundations of longitudinal and panel data analysis at a level suitable for quantitatively oriented graduate social science students as well as individual researchers. He emphasizes mathematical and statistical fundamentals but also demonstrates substantive applications from across the social sciences. These applications are enhanced by real-world data sets and software programs in SAS and Stata.

A straightforward, comprehensive, and approachable guide to research as practiced by social scientists, the Fourteenth Edition of this "gold-standard" book gives readers the tools they need to apply research concepts practically, as both a researcher and a consumer. The author emphasizes the process by showing readers how to design and construct projects, introducing the various observation modes in use today, and answering critical questions about research methods-such as how to conduct online surveys and analyze both qualitative and quantitative data.