Bayesian methods are increasingly becoming attractive to researchers in many fields. Econometrics, however, is a field in which Bayesian methods have had relatively less influence. A key reason for this absence is the lack of a suitable advanced undergraduate or graduate level textbook. Existing Bayesian books are either out-dated, and hence do not cover the computational advances that have revolutionized the field of Bayesian econometrics since the late 1980s, or do not provide the broad coverage necessary for the student interested in empirical work applying Bayesian methods. For instance, Arnold Zellner’s seminal Bayesian econometrics book (Zellner, 1971) was published in 1971. Dale Poirier’s influential book (Poirier, 1995) focuses on the methodology and statistical theory underlying Bayesian and frequentist methods, but does not discuss models used by applied economists beyond regression. Other important Bayesian books, such as Bauwens, Lubrano and Richard (1999), deal only with particular areas of econometrics (e.g. time series models). In writing this book, my aim has been to fill the gap in the existing set of Bayesian textbooks, and create a Bayesian counterpart to the many popular non-Bayesian econometric textbooks now available (e.g. Greene, 1995). That is, my aim has been to write a book that covers a wide range of models and prepares the student to undertake applied work using Bayesian methods. This book is intended to be accessible to students with no prior training in econometrics, and only a single course in mathematics (e.g. basic calculus). Students will find a previous undergraduate course in probability and statistics useful; however Appendix B offers a brief introduction to these topics for those without the prerequisite background. Throughout the book, I have tried to keep the level of mathematical sophistication reasonably low. In contrast to other Bayesian and comparable frequentist textbooks, I have included more computer-related material. Modern Bayesian econometrics relies heavily on the computer, and developing some basic programming skills is essential for the applied Bayesian. The required level of computer programming skills is not that high, but I expect that this aspect of Bayesian econometrics might be most unfamiliar to the student brought up in the world of spreadsheets and click-and-press computer packages. Accordingly, in addition to discussing computation in detail in the book itself, the website associated with the book contains MATLAB programs for performing Bayesian analysis in a wide variety of models. In general, the focus of the book is on application rather than theory. Hence, I expect that the applied economist interested in using Bayesian methods will find it more useful than the theoretical econometrician.

计量经济学 主要目标是使用MATLAB和Python（在项目中）实现计量经济学方法。 我特别感兴趣的方法是那些在那些程序中未完全实现的方法，并且在我看来，这些方法对于经济研究非常有用。 此存储库中的前两个项目是“简单引导程序包”和“ SVAR”。 第一个包含引导程序技术，可处理随机样本和相关数据，而第二个则是使用短期和长期限制来估计本地标识的结构VAR。 我已经记录了每个项目（.pdf文件），以简单的方式展示了我们如何实现这些方法。 阅读“ SVAR.pdf”，“一个简单的Bootstrap软件包.pdf”，“关于Monte Carlo模拟的注意事项[Spanish] .pdf”和“关于计量经济学的注意事项I [Spanish] .pdf”。 欢迎提出意见，建议和批评家。 贡献者 亚历克斯·卡拉斯科（Alex Carrasco）/

Econometric Analysis of Cross Section and Panel Data课后答案

ECONOMETRIC ANALYSIS EIGHTH EDITION William H. Greene

Statistical and Econometric Methods for Transportation Data Analysis

Econometric Analysis of Cross Section and Panel Data 第二版 高清英文 Jeffrey M. Wooldridge The MIT Press

任务 计量经济学软件简介：Stata 作业

1969-诺奖得主Granger-Investigating Causal Relations by Econometric Models and Cross-spectral Methods

[James Davidson] Econometric theory