Panel Methods for Finance: A Guide to Panel Data Econometrics for Financial Applications, Marno Verbeek
Автор: Arakawa Hiromu Название: Fullmetal Alchemist: The Complete Four-Panel Comics ISBN: 1974706176 ISBN-13(EAN): 9781974706174 Издательство: Viz Media Рейтинг: Цена: 9890.00 T Наличие на складе: Ожидается поступление. Описание: Alchemy tore the Elric brothers` bodies apart. Can their bond make them whole again?
A brand new, fully updated edition of a popular classic on matrix differential calculus with applications in statistics and econometrics
This exhaustive, self-contained book on matrix theory and matrix differential calculus provides a treatment of matrix calculus based on differentials and shows how easy it is to use this theory once you have mastered the technique. Jan Magnus, who, along with the late Heinz Neudecker, pioneered the theory, develops it further in this new edition and provides many examples along the way to support it.
Matrix calculus has become an essential tool for quantitative methods in a large number of applications, ranging from social and behavioral sciences to econometrics. It is still relevant and used today in a wide range of subjects such as the biosciences and psychology. Matrix Differential Calculus with Applications in Statistics and Econometrics, Third Edition contains all of the essentials of multivariable calculus with an emphasis on the use of differentials. It starts by presenting a concise, yet thorough overview of matrix algebra, then goes on to develop the theory of differentials. The rest of the text combines the theory and application of matrix differential calculus, providing the practitioner and researcher with both a quick review and a detailed reference.
Fulfills the need for an updated and unified treatment of matrix differential calculus
Contains many new examples and exercises based on questions asked of the author over the years
Covers new developments in field and features new applications
Written by a leading expert and pioneer of the theory
Part of the Wiley Series in Probability and Statistics
Matrix Differential Calculus With Applications in Statistics and Econometrics Third Edition is an ideal text for graduate students and academics studying the subject, as well as for postgraduates and specialists working in biosciences and psychology.
Financial data are typically characterised by a time-series dimension and a cross-sectional dimension. For example, we may observe financial information on a group of firms over a number of years, or we may observe returns of all stocks traded at NYSE over a period of 120 months. Accordingly, econometric modelling in finance requires appropriate attention to these two -- or occasionally more than two -- dimensions of the data. Panel data techniques are developed to do exactly this. This book provides an overview of commonly applied panel methods for financial applications.
The use of panel data has many advantages, in terms of the flexibility of econometric modeling and the ability to control for unobserved heterogeneity. It also involves a number of econometric issues that require specific attention. This includes cross-sectional dependence, robust and clustered standard errors, parameter heterogeneity, fixed effects, dynamic models with a short time dimension, instrumental variables, differences-in-differences and other approaches for causal inference.
After an introductory chapter reviewing the classical linear regression model with particular attention to its use in a panel data context, including several standard estimators (pooled OLS, Fama-MacBeth, random effects, first-differences, fixed effects), the book continues with a more elaborate treatment of fixed effects approaches. While first-differencing and fixed effects estimators are attractive because of their removal of time-invariant unobserved heterogeneity (e.g. manager quality, firm culture), consistency of such estimators imposes strict exogeneity of the explanatory variables (for a finite number of time periods). This is often violated in practice, for example, some explanatory variable explaining firm performance may be partly determined by historical firm performance. An obvious case where this assumption is violated arises when the model contains a lagged dependent variable. A separate chapter will focus on dynamic models, which have received specific attention in the literature, also in the context of financial applications, like the dynamics of capital structure choices. Estimation mostly relies on instrumental variables or GMM techniques. Identification and estimation of such models is often fragile, and the small sample properties may be disappointing.
The book continues with a chapter on models with limited dependent variables, including binary response models. The cross-sectional dependence that is likely to be present complicates estimation, and the author discusses pooled estimation, random effects and fixed effects approaches, including the possibility to include lagged dependent variables. This chapter will also discuss problems of attrition and sample selection bias, as well as unbalanced panels in general.
Identifying causal effects in empirical work based on non-experimental data is often challenging, and causal inference has received substantial attention in the recent literature. The availability of panel data plays an important role in many approaches. Starting with simple differences-in-differences approaches, a dedicated chapter discusses instrumental variables estimators, matching and propensity scores, regression discontinuity and related approaches.
Автор: Laszlo Matyas Название: The Econometrics of Panel Data: Handbook of Theory and Applications ISBN: 9401066558 ISBN-13(EAN): 9789401066556 Издательство: Springer Рейтинг: Цена: 81050.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The aim of this volume is to provide a general overview of the econometrics of panel data, both from a theoretical and from an applied viewpoint. Part II deals with nonlinear models and related issues: logit and probit models, latent variable models, incomplete panels and selectivity bias, and point processes.
Автор: Wooldridge JM Название: Econometric analysis of cross section and panel data 2e ISBN: 0262232588 ISBN-13(EAN): 9780262232586 Издательство: MIT Press Рейтинг: Цена: 129790.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
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.
Автор: Beenstock, Michael, Felsenstein, Daniel Название: The Econometric Analysis of Non-Stationary Spatial Panel Data ISBN: 3030036138 ISBN-13(EAN): 9783030036133 Издательство: Springer Рейтинг: Цена: 93160.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This monograph deals with spatially dependent non-stationary time series in a way accessible to both time series econometricians wanting to understand spatial econometics, and spatial econometricians lacking a grounding in time series analysis.
This book aims to fill the gap between panel data econometrics textbooks, and the latest development on "big data", especially large-dimensional panel data econometrics. It introduces important research questions in large panels, including testing for cross-sectional dependence, estimation of factor-augmented panel data models, structural breaks in panels and group patterns in panels. To tackle these high dimensional issues, some techniques used in Machine Learning approaches are also illustrated. Moreover, the Monte Carlo experiments, and empirical examples are also utilised to show how to implement these new inference methods. Large-Dimensional Panel Data Econometrics: Testing, Estimation and Structural Changes also introduces new research questions and results in recent literature in this field.
Автор: L?szl? M?ty?s; Patrick Sevestre Название: The Econometrics of Panel Data ISBN: 9401065489 ISBN-13(EAN): 9789401065481 Издательство: Springer Рейтинг: Цена: 81050.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Part II deals with nonlinear models and related issues: logit and pro bit models, latent variable models, duration and count data models, incomplete panels and selectivity bias, point processes, and simulation techniques.
Автор: Hsiao, Cheng, Название: Analysis of Panel Data ISBN: 1107657636 ISBN-13(EAN): 9781107657632 Издательство: Cambridge Academ Рейтинг: Цена: 39060.00 T Наличие на складе: Поставка под заказ. Описание: This book provides a comprehensive, coherent, and intuitive review of panel data methodologies that are useful for empirical analysis. Substantially revised from the second edition, it includes two new chapters on modeling cross-sectionally dependent data and dynamic systems of equations. Some of the more complicated concepts have been further streamlined. Other new material includes correlated random coefficient models, pseudo-panels, duration and count data models, quantile analysis, and alternative approaches for controlling the impact of unobserved heterogeneity in nonlinear panel data models.
Автор: Sul Название: Panel Data Econometrics ISBN: 1138389676 ISBN-13(EAN): 9781138389670 Издательство: Taylor&Francis Рейтинг: Цена: 47970.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: In the last 20 years, econometric theory on panel data has developed rapidly, particularly for analyzing common behaviors among individuals over time. Meanwhile the statistical methods employed by researchers have not kept up-to-date. This book attempts to fill this gap, teaching researchers how to use the latest panel estimation methods correctly.
Автор: Marc Nerlove Название: Essays in Panel Data Econometrics ISBN: 0521815347 ISBN-13(EAN): 9780521815345 Издательство: Cambridge Academ Рейтинг: Цена: 85530.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This volume collects seven of Marc Nerlove`s previously published, classic essays on panel data econometrics, and a cogent essay on the history of the subject. The essays illustrate both the role of the substantive context in shaping appropriate methods of inference and the increasing importance of computer-intensive methods.
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