This book provides a broad, mature, and systematic introduction to current financial econometric models and their applications to modeling and prediction of financial time series data. It utilizes real-world examples and real financial data throughout the book to apply the models and methods described. The author begins with basic characteristics of financial time series data before covering three main topics: Analysis and application of univariate financial time series The return series of multiple assets Bayesian inference in finance methods Key features of the new edition include additional coverage of modern day topics such as arbitrage, pair trading, realized volatility, and credit risk modeling; a smooth transition from S-Plus to R; and expanded empirical financial data sets. The overall objective of the book is to provide some knowledge of financial time series, introduce some statistical tools useful for analyzing these series and gain experience in financial applications of various econometric methods.

The book is a collection of essays in honour of Clive Granger. The chapters are by some of the world'leading econometricians, all of whom have collaborated with or studied with (or both) Clive Granger. Central themes of Grangers work are reflected in the book with attention to tests for unit roots and cointegration, tests of misspecification, forecasting models and forecast evaluation, non-linear and non-parametric econometric techniques, and overall, a careful blend of practical empirical work and strong theory. The book shows the scope of Granger's research and the range of the profession that has been influenced by his work.

《计量经济学导论(第3册)》从计量经济学专业人士的视角来讲授计量经济学导论，不仅使这门学科更有意思，而且实际上讲解起来还更简单。其内容有：计量经济学的性质与经济数据、简单回归模型等。

《计量经济学基础(上下)》(第4版)十分重视基础知识的教学及训练，内容深入浅出。第四版新改进之处：（1）“线性回归的矩阵表述”部分有所压缩；（2）有关“计睛经济建模”的章节有所精简；（3）新增了“非线性回归模型”一章。（4）新增了一些“综例数据回归模型”的方面的材料。

This graduate text provides an intuitive but rigorous treatment of contemporary methods used in microeconometric research. The book makes clear that applied microeconometrics is about the estimation of marginal and treatment effects, and that parametric estimation is simply a means to this end. It also clarifies the distinction between causality and statistical association.<br /> <br /> The book focuses specifically on cross section and panel data methods. Population assumptions are stated separately from sampling assumptions, leading to simple statements as well as to important insights. The unified approach to linear and nonlinear models and to cross section and panel data enables straightforward coverage of more advanced methods. The numerous end-of-chapter problems are an important component of the book. Some problems contain important points not fully described in the text, and others cover new ideas that can be analyzed using tools presented in the current and previous chapters. Several problems require the use of the data sets located at the author's website.

Practical and professional, this text bridges the gap between how undergraduate econometrics has traditionally been taught and how empirical researchers actually think about and apply econometric methods. The text's unique approach reflects how econometric instruction has evolved from simply describing a set of abstract recipes to showing how econometrics can be used to empirically study questions across a variety of disciplines. The systematic approach, where assumptions are introduced only as they are needed to obtain a certain result, makes the material easier for students, and leads to better econometric practice. It is organised around the type of data being analysed - an approach that simplifies the exposition and allows a more careful discussion of assumptions. Packed with relevant applications and a wealth of interesting data sets, the text emphasises examples that have implications for policy or provide evidence for or against economic theories.

《计量经济学基础(第5版)(上下册)》是一本经典的初级计量经济学教材，第一版问世至今已有三十年。对于初涉计量经济学而又没有太多数学背景的读者来说，这本书可以帮助你在短时间内了解计量经济学的脉络。本书的主要特点是：
(1)读者不需要高深的数学知识，只要具备基本的数学知识就可以阅读本书；
(2)运用大量的经济计量模型实例，特别是图形进行分析，易于读者的理解；
(3)书中突出强调了计量经济学对经济和金融数据的应用分析，一些模型的引用对相关专业的读者解决实际问题很有指导意义。

This book provides the tools and concepts necessary to study the behavior of econometric estimators and test statistics in large samples. An econometric estimator is a solution to an optimization problem; that is, a problem that requires a body of techniques to determine a specific solution in a defined set of possible alternatives that best satisfies a selected object function or set of constraints. Thus, this highly mathematical book investigates situations concerning large numbers, in which the assumptions of the classical linear model fail. Economists, of course, face these situations often. It includes completely revised chapter seven on functional central limit theory and its applications, specifically unit root regression, spurious regression, and regression with cointegrated processes. It includes updated material on: central limit theory; asymptotically efficient instrumental variables estimation; estimation of asymptotic covariance matrices; efficient estimation with estimated error covariance matrices; and efficient IV estimation.

The core methods in today's econometric toolkit are linear regression for statistical control, instrumental variables methods for the analysis of natural experiments, and differences-in-differences methods that exploit policy changes. In the modern experimentalist paradigm, these techniques address clear causal questions such as: Do smaller classes increase learning? Should wife batterers be arrested? How much does education raise wages? Mostly Harmless Econometrics shows how the basic tools of applied econometrics allow the data to speak.
In addition to econometric essentials, Mostly Harmless Econometrics covers important new extensions--regression-discontinuity designs and quantile regression--as well as how to get standard errors right. Joshua Angrist and Jörn-Steffen Pischke explain why fancier econometric techniques are typically unnecessary and even dangerous. The applied econometric methods emphasized in this book are easy to use and relevant for many areas of contemporary social science.
* An irreverent review of econometric essentials
* A focus on tools that applied researchers use most
* Chapters on regression-discontinuity designs, quantile regression, and standard errors
* Many empirical examples
* A clear and concise resource with wide applications

Until now, students and researchers in nonparametric and semiparametric statistics and econometrics have had to turn to the latest journal articles to keep pace with these emerging methods of economic analysis. "Nonparametric Econometrics" fills a major gap by gathering together the most up-to-date theory and techniques and presenting them in a remarkably straightforward and accessible format. The empirical tests, data, and exercises included in this textbook help make it the ideal introduction for graduate students and an indispensable resource for researchers. Nonparametric and semiparametric methods have attracted a great deal of attention from statisticians in recent decades. While the majority of existing books on the subject operate from the presumption that the underlying data is strictly continuous in nature, more often than not social scientists deal with categorical data-nominal and ordinal - in applied settings. The conventional nonparametric approach to dealing with the presence of discrete variables is acknowledged to be unsatisfactory. This book is tailored to the needs of applied econometricians and social scientists. Qi Li and Jeffrey Racine emphasize nonparametric techniques suited to the rich array of data types -continuous, nominal, and ordinal - within one coherent framework. They also emphasize the properties of nonparametric estimators in the presence of potentially irrelevant variables. "Nonparametric Econometrics" covers all the material necessary to understand and apply nonparametric methods for real-world problems.

Quantile regression is gradually emerging as a unified statistical methodology for estimating models of conditional quantile functions. By complementing the exclusive focus of classical least squares regression on the conditional mean, quantile regression offers a systematic strategy for examining how covariates influence the location, scale and shape of the entire response distribution. This monograph is the first comprehensive treatment of the subject, encompassing models that are linear and nonlinear, parametric and nonparametric. The author has devoted more than 25 years of research to this topic. The methods in the analysis are illustrated with a variety of applications from economics, biology, ecology and finance. The treatment will find its core audiences in econometrics, statistics, and applied mathematics in addition to the disciplines cited above.

Principles of Econometrics clearly shows why econometrics is necessary and provides you with the ability to utilize basic econometric tools. You'll learn how to apply these tools to estimation, inference, and forecasting in the context of real world economic problems. In order to make concepts more accessible, the authors offer lucid descriptions of techniques as well as appropriate applications to today's situations. Along the way, you'll find introductions to simple economic models and questions to enhance critical thinking.

When John Maynard Keynes likened Jan Tinbergen's early work in econometrics to black magic and alchemy, he was expressing a widely held view of a new discipline. However, even after half a century of practical work and theorizing by some of the most accomplished social scientists, Keynes' comments are still repeated today. This book assesses the foundations and development of econometrics and sets out a basis for the reconstruction of the foundations of econometric inference by examining the various interpretations of probability theory which underlie econometrics. Keuzenkamp claims that the probabilistic foundations of econometrics are weak, and although econometric inferences may yield interesting knowledge, claims to be able to falsify or verify economic theories are unwarranted. Methodological falsificationism in econometrics is an illusion. Instead, it is argued, econometrics should locate itself in the tradition of positivism.

This major new textbook from a distinguished econometrician is intended for students taking introductory courses in probability theory and statistical inference. No prior knowledge other than a basic familiarity with descriptive statistics is assumed. The primary objective of this book is to establish the framework for the empirical modelling of observational (non-experimental) data. This framework known as 'Probabilistic Reduction' is formulated with a view to accommodating the peculiarities of observational (as opposed to experimental) data in a unifying and logically coherent way. Probability Theory and Statistical Inference differs from traditional textbooks in so far as it emphasizes concepts, ideas, notions and procedures which are appropriate for modelling observational data. Aimed at students at second-year undergraduate level and above studying econometrics and economics, this textbook will also be useful for students in other disciplines which make extensive use of observational data, including finance, biology, sociology and psychology and climatology.

This volume explains recent theoretical developments in the econometric modelling of relationships between different statistical series. The statistical techniques explored analyse relationships between different variables, over time, such as the relationship between variables in a macroeconomy. Examples from Professor Terasvirta's empirical work are given. Professors Granger and Terasvirta are leading exponents of techniques of dynamic, multivariate analysis. They illustrate in this volume exploratory ways of using such techniques to provide models of nonlinear relationships between variables. This is an extension of previous work on linear relationships, and on univariate models. These developments will be of use to econometricians wishing to construct and use models of nonlinear, dynamic, multivariate relationships, such as an investment function, or a production function. Particular attention is paid to the case of a single dependent variable modelled by a few explanatory variables and the lagged dependent variable in nonlinear form. The book concentrates on stochastic series, since the existence of unexpected shocks strongly suggests that economic variables are stochastic. Granger and Terasvirta also discuss the division of these nonlinear relationships into parametric and nonparametric models.

This book, and its companion volume in the Econometric Society Monographs series (ESM number 32), present a collection of papers by Clive W. J. Granger. His contributions to economics and econometrics, many of them seminal, span more than four decades and touch on all aspects of time series analysis. The papers assembled in this volume explore topics in causality, integration and cointegration, and long memory. Those in the companion volume investigate themes in causality, integration and cointegration, and long memory. The two volumes contain the original articles as well as an introduction written by the editors.

The History of Econometric Ideas covers the period from the late nineteenth century to the middle of the twentieth century, illustrating how economists first learned to harness statistical methods to measure and test the "laws" of economics. Though scholarly, Dr. Morgan's book is very accessible; it does not require a high level of prior statistical knowledge, and will be of interest to practicing statisticians and economists.

"Econometrics" explains the relationship of econometrics to economics and statistics; outlines the process of formulating economic hypotheses mathematically and of subjecting them to a statistical test; deals with the various component equations of the economic system; and illustrates the use of econometric methods for policy purposes.